Date: Thu Mar 26 04:36:03 2020
Scientist: Ran Yin
Sequencing (Waksman): Dibyendu Kumar
Statistics: Davit Sargsyan
Principal Investigator: Ah-Ng Kong
# Taxonomic Ranks:
# **K**ing **P**hillip **C**an n**O**t **F**ind **G**reen **S**ocks
# * Kingdom
# * Phylum
# * Class
# * Order
# * Family
# * Genus
# * Species
# options(stringsAsFactors = FALSE,
# scipen = 999)
# # Increase mmemory size to 64 Gb----
# invisible(utils::memory.limit(65536))
# str(knitr::opts_chunk$get())
# # NOTE: the below does not work!
# knitr::opts_chunk$set(echo = FALSE,
# message = FALSE,
# warning = FALSE,
# error = FALSE)
# require(knitr)
# require(kableExtra)
# require(shiny)
require(phyloseq)
Loading required package: phyloseq
require(data.table)
Loading required package: data.table
data.table 1.12.2 using 18 threads (see ?getDTthreads). Latest news: r-datatable.com
require(ggplot2)
Loading required package: ggplot2
require(plotly)
Loading required package: plotly
Attaching package: ‘plotly’
The following object is masked from ‘package:ggplot2’:
last_plot
The following object is masked from ‘package:stats’:
filter
The following object is masked from ‘package:graphics’:
layout
require(DT)
Loading required package: DT
require(lmerTest)
Loading required package: lmerTest
Loading required package: lme4
Loading required package: Matrix
Attaching package: ‘lmerTest’
The following object is masked from ‘package:lme4’:
lmer
The following object is masked from ‘package:stats’:
step
require(nnet)
Loading required package: nnet
source("source/functions_may2019.R")
# On Windows set multithread=FALSE----
mt <- TRUE
Introduction
C57BL/6 wild-type (WT) and Nrf-2 double-knock-out (KO -/-) mice were given 2-week microbiome stabilization process using AIN93M diet and 8 more weeks to treat with either AIN93M or AIN93M 5% PEITC diet. Fecal samples were collected weekly, immediately frozen in liquid nitrogen and stored at -80oC. Serum, cecal, colon epithelial and whole colon tissues at week 10 were also collected for further analyses. Baseline, week 1 and 4 fecal samples were selected for 16s rRNA sequencing.
This document examines results from the WT mice samples.
We will attampt to answer the following questions:
1. Did microbiome change over time?
2. Was microbiome affected by diet?
3. Was there a difference between the KO and WT?
4. If there was a change in microbiome composition, what functional changes did it carry? What are the essential functions of the bacteria affected by the treatment and how can this be shown in vivo (metabolites, inflammation markers, etc.)?
Data preprocessing
Raw Data
FastQ files were downloaded from this Rutgers Box location. A total of 144 files (2 per sample, pair-ended) and a pair of undetermined reads were downloaded.
Samples
ps_sep2019@sam_data$Genotype_Week <- paste(ps_sep2019@sam_data$genotype,
ps_sep2019@sam_data$time,
sep = "_")
ps_sep2019@sam_data$ID <- factor(paste0(ps_sep2019@sam_data$mice_num,
ps_sep2019@sam_data$cage))
ps_sep2019@sam_data$TREATMENT <- paste0(ps_sep2019@sam_data$DSS,
ps_sep2019@sam_data$PEITC,
ps_sep2019@sam_data$cranberry)
ps_sep2019@sam_data$TREATMENT <- factor(ps_sep2019@sam_data$TREATMENT,
levels = c("000",
"100",
"110",
"101"),
labels = c("Naive",
"DSS",
"DSS+PEITC",
"DSS+Cranberry"))
samples <- ps_sep2019@sam_data
datatable(samples,
options = list(pageLength = nrow(samples)))
Prune data
The OTUs were mapped to Bacteria (96.07%), Eukaryota (2.95%) and Archea (0.03%) kingdoms, and 75 OTUs (0.95%) undefined.
The total of 7,867 unique sequences were found. Out of those, 7,558 were mapped to bacterial genomes.
dim(ps_sep2019@otu_table@.Data)
# Remove OTU not mapped to Bacteria
ps0 <- subset_taxa(ps_sep2019,
Kingdom == "Bacteria")
dim(ps0@otu_table@.Data)
Out of the 7,558 OTUs 7,247 belonged to 12 Phyla. 311 of the OTUs (or 4.11% of bacterial OTUs) could not be mapped to a phylum.
t2 <- data.table(table(tax_table(ps0)[, "Phylum"],
exclude = NULL))
t2$V1[is.na(t2$V1)] <- "Unknown"
setorder(t2, -N)
t2[, pct := N/sum(N)]
setorder(t2, -N)
colnames(t2) <- c("Phylum",
"Number of OTUs",
"Percent of OTUs")
datatable(t2,
rownames = FALSE,
caption = "Number of Bacterial OTUs by Phylum",
class = "cell-border stripe",
options = list(search = FALSE,
pageLength = nrow(t2))) %>%
formatCurrency(columns = 2,
currency = "",
mark = ",",
digits = 0) %>%
formatPercentage(columns = 3,
digits = 2)
OTU table (first 10 rows)
Total counts per sample (i.e. sequencing depth)
t1 <- colSums(otu[, 7:ncol(otu)])
t1 <- data.table(SAMPLE_NAME = names(t1),
Total = t1)
t2 <- data.table(SAMPLE_NAME = rownames(samples),
ID = samples$ID,
CAGE = samples$cage,
TREATMENT = samples$TREATMENT,
Genotype = samples$genotype,
WEEK = samples$time)
smpl <- merge(t1,
t2,
by = "SAMPLE_NAME")
p1 <- ggplot(smpl,
aes(x = SAMPLE_NAME,
y = Total,
fill = TREATMENT,
colour = WEEK)) +
facet_wrap(~ Genotype,
scale = "free_x") +
geom_bar(stat = "identity") +
scale_x_discrete("") +
scale_y_continuous("Number of Reads") +
scale_fill_discrete("Treatment") +
theme(axis.text.x = element_text(angle = 45,
hjust = 1))
ggplotly(p1)
tmp <- copy(smpl)
tmp$WEEK <- factor(tmp$WEEK,
levels = c("baseline",
"week1",
"week8"),
labels = c("Week 0",
"Week 1",
"Week 8"))
tmp$Genotype <- factor(tmp$Genotype,
levels = c("widetype",
"nrf2KO"),
labels = c("Wild Type",
"Nrf2 KO"))
p1 <- ggplot(tmp,
aes(x = SAMPLE_NAME,
y = Total,
group = TREATMENT,
fill = TREATMENT)) +
facet_wrap(~ Genotype + WEEK,
scale = "free_x") +
geom_bar(stat = "identity",
color = "black") +
scale_x_discrete("") +
scale_y_continuous("Number of Reads") +
scale_fill_grey("Treatment",
start = 0.1,
end = 1,
na.value = "red",
aesthetics = "fill") +
theme_bw() +
theme(panel.border = element_blank(),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.title.x=element_blank(),
axis.text.x=element_blank(),
axis.ticks.x=element_blank(),
legend.position = "top")
tiff(filename = "tmp/seq_depth.tiff",
height = 6,
width = 6,
units = "in",
res = 600,
compression = "lzw+p")
print(p1)
graphics.off()
print(p1)
Richness (Alpha diversity)
Shannon index (aka Shannon enthrophy) is calculated as:
H’ = -sum(1 to R)p(i)ln(p(i)) When there is exactly 1 type of data (e.g. a single species in the sample), H’=0. The opposite scenario is when there are R>1 species present in the sample in the exact same amounts and H’=ln(R).
Shannon’s diversity index was calculated for each sample and ploted over time using the 7,764 from the 13 Phylum above.
shannon.ndx <- estimate_richness(ps0,
measures = "Shannon")
shannon.ndx <- data.table(SAMPLE_NAME = rownames(shannon.ndx),
shannon.ndx)
smpl <- merge(smpl,
shannon.ndx,
by = "SAMPLE_NAME")
p1 <- ggplot(smpl,
aes(x = Total,
y = Shannon,
fill = Genotype,
shape = WEEK)) +
geom_point(size = 2) +
scale_shape_manual(breaks = unique(smpl$WEEK),
values = 21:23)
tiff(filename = "tmp/shannon_vs_depth.tiff",
height = 5,
width = 6,
units = "in",
res = 600,
compression = "lzw+p")
print(p1)
graphics.off()
ggplotly(p1)
Even though estimate_richness function does not adjust for the sequencing depth, there is no correlation between the index and the sample’s sequecing depth. Proceed with the comparison.
Shannon idex over time
p1 <- plot_richness(ps0,
x = "time",
measures = "Shannon") +
facet_wrap(~ genotype) +
geom_line(aes(group = ID),
color = "black") +
geom_point(aes(fill = TREATMENT),
shape = 21,
size = 3,
color = "black") +
scale_x_discrete("") +
theme(axis.text.x = element_text(angle = 30,
hjust = 1,
vjust = 1))
ggplotly(p = p1,
tooltip = c("ID",
"value"))
p1 <- p1 +
scale_fill_discrete("") +
theme(legend.position = "top")
tiff(filename = "tmp/shannon.tiff",
height = 4,
width = 5,
units = "in",
res = 600,
compression = "lzw+p")
print(p1)
graphics.off()
The plot above suggests that the largest differences in alpha diversity (as measured by Shannon’s index) are in genotype.
Average Shannon Index
# Average shannon index by treatment group
tmp <- copy(smpl)
tmp[, mu := mean(Shannon),
by = list(TREATMENT,
Genotype,
WEEK)]
tmp[, sem := sd(Shannon)/sqrt(.N),
by = list(TREATMENT,
Genotype,
WEEK)]
tmp <- unique(tmp[, c("TREATMENT",
"Genotype",
"WEEK",
"mu",
"sem")])
tmp$WEEK <- factor(tmp$WEEK,
levels = c("baseline",
"week1",
"week8"),
labels = c("Week 0",
"Week 1",
"Week 8"))
tmp$Genotype <- factor(tmp$Genotype,
levels = c("widetype",
"nrf2KO"),
labels = c("Wild Type",
"Nrf2 KO"))
p1 <- ggplot(tmp,
aes(x = WEEK,
y = mu,
ymin = mu - sem,
ymax = mu + sem,
fill = TREATMENT,
group = TREATMENT)) +
facet_wrap(~ Genotype) +
geom_errorbar(position = position_dodge(0.4),
width = 0.4) +
geom_line(position = position_dodge(0.4)) +
geom_point(size = 3,
shape = 21,
position = position_dodge(0.4)) +
scale_x_discrete("") +
scale_y_continuous("Shannon Index") +
theme(axis.text.x = element_text(angle = 45,
hjust = 1),
legend.position = "top")
tiff(filename = "tmp/avg_shannon.tiff",
height = 5,
width = 6,
units = "in",
res = 600,
compression = "lzw+p")
print(p1)
graphics.off()
print(p1)
Test if the richness changed between the baseline and Week 8.
smpl$TREATMENT <- factor(smpl$TREATMENT,
levels = c("DSS",
"Naive",
"DSS+PEITC",
"DSS+Cranberry"))
tmp <- droplevels(smpl[WEEK != "week1"])
m1 <- lm(Shannon ~ WEEK*(TREATMENT + Genotype),
# offset = Total,
data = tmp)
summary(m1)
Call:
lm(formula = Shannon ~ WEEK * (TREATMENT + Genotype), data = tmp)
Residuals:
Min 1Q Median 3Q Max
-0.316186 -0.091027 0.007886 0.110704 0.293230
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.94987 0.07064 84.233 < 2e-16 ***
WEEKweek8 0.01158 0.09989 0.116 0.9084
TREATMENTNaive 0.14581 0.08935 1.632 0.1109
TREATMENTDSS+PEITC -0.03923 0.08935 -0.439 0.6631
TREATMENTDSS+Cranberry -0.22582 0.08935 -2.527 0.0158 *
Genotypewidetype -0.54156 0.06318 -8.572 2.06e-10 ***
WEEKweek8:TREATMENTNaive 0.01181 0.12636 0.093 0.9261
WEEKweek8:TREATMENTDSS+PEITC 0.01652 0.12636 0.131 0.8966
WEEKweek8:TREATMENTDSS+Cranberry 0.21535 0.12636 1.704 0.0965 .
WEEKweek8:Genotypewidetype 0.23085 0.08935 2.584 0.0137 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1548 on 38 degrees of freedom
Multiple R-squared: 0.7845, Adjusted R-squared: 0.7335
F-statistic: 15.37 on 9 and 38 DF, p-value: 3.671e-10
m2 <- lmer(Shannon ~ WEEK*(TREATMENT + Genotype) + (1 | ID),
# offset = Total,
data = tmp)
summary(m2)
Linear mixed model fit by REML. t-tests use Satterthwaite's method ['lmerModLmerTest']
Formula: Shannon ~ WEEK * (TREATMENT + Genotype) + (1 | ID)
Data: tmp
REML criterion at convergence: -22.2
Scaled residuals:
Min 1Q Median 3Q Max
-1.53721 -0.47495 0.06753 0.44489 1.53874
Random effects:
Groups Name Variance Std.Dev.
ID (Intercept) 0.01259 0.1122
Residual 0.01136 0.1066
Number of obs: 48, groups: ID, 24
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.94987 0.07064 29.76837 84.233 < 2e-16 ***
WEEKweek8 0.01158 0.06879 19.00000 0.168 0.86814
TREATMENTNaive 0.14581 0.08935 29.76837 1.632 0.11322
TREATMENTDSS+PEITC -0.03923 0.08935 29.76837 -0.439 0.66379
TREATMENTDSS+Cranberry -0.22582 0.08935 29.76837 -2.527 0.01704 *
Genotypewidetype -0.54156 0.06318 29.76837 -8.572 1.55e-09 ***
WEEKweek8:TREATMENTNaive 0.01181 0.08701 19.00000 0.136 0.89350
WEEKweek8:TREATMENTDSS+PEITC 0.01652 0.08701 19.00000 0.190 0.85139
WEEKweek8:TREATMENTDSS+Cranberry 0.21535 0.08701 19.00000 2.475 0.02291 *
WEEKweek8:Genotypewidetype 0.23085 0.06152 19.00000 3.752 0.00135 **
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Correlation of Fixed Effects:
(Intr) WEEKw8 TREATMENTN TREATMENTDSS+P TREATMENTDSS+C Gntypw
WEEKweek8 -0.487
TREATMENTNv -0.632 0.308
TREATMENTDSS+P -0.632 0.308 0.500
TREATMENTDSS+C -0.632 0.308 0.500 0.500
Gentypwdtyp -0.447 0.218 0.000 0.000 0.000
WEEK8:TREATMENTN 0.308 -0.632 -0.487 -0.243 -0.243 0.000
WEEK8:TREATMENTDSS+P 0.308 -0.632 -0.243 -0.487 -0.243 0.000
WEEK8:TREATMENTDSS+C 0.308 -0.632 -0.243 -0.243 -0.487 0.000
WEEKwk8:Gnt 0.218 -0.447 0.000 0.000 0.000 -0.487
WEEK8:TREATMENTN WEEK8:TREATMENTDSS+P WEEK8:TREATMENTDSS+C
WEEKweek8
TREATMENTNv
TREATMENTDSS+P
TREATMENTDSS+C
Gentypwdtyp
WEEK8:TREATMENTN
WEEK8:TREATMENTDSS+P 0.500
WEEK8:TREATMENTDSS+C 0.500 0.500
WEEKwk8:Gnt 0.000 0.000 0.000
Calculate change in Shannon index from baseline
dd <- smpl
dd[, delta := Shannon - Shannon[WEEK == "baseline"],
by = ID]
dd$diff <- paste(dd$WEEK,
"-baseline",
sep = "")
dd <- dd[WEEK != "baseline",]
p1 <- ggplot(dd,
aes(x = TREATMENT,
y = delta,
fill = Genotype)) +
facet_wrap(~ diff) +
geom_hline(yintercept = 0,
linetype = "dashed") +
geom_point(position = position_dodge(0.3),
shape = 21,
size = 3) +
scale_y_continuous("Shannon Index Percent Change from Baseline") +
theme(axis.text.x = element_text(angle = 45,
hjust = 1))
print(p1)

dd$TREATMENT <- factor(dd$TREATMENT,
levels = c("DSS",
"Naive",
"DSS+PEITC",
"DSS+Cranberry"))
dd$Genotype <- factor(dd$Genotype,
levels = c("widetype",
"nrf2KO"))
m1 <- lm(delta ~ TREATMENT*Genotype,
data = dd)
summary(m1)
Call:
lm(formula = delta ~ TREATMENT * Genotype, data = dd)
Residuals:
Min 1Q Median 3Q Max
-0.40513 -0.09560 -0.02012 0.09568 0.35517
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.25142 0.07286 3.451 0.00133 **
TREATMENTNaive -0.04426 0.10303 -0.430 0.66985
TREATMENTDSS+PEITC -0.15777 0.10303 -1.531 0.13358
TREATMENTDSS+Cranberry 0.04463 0.10303 0.433 0.66720
Genotypenrf2KO -0.18412 0.10303 -1.787 0.08153 .
TREATMENTNaive:Genotypenrf2KO -0.02851 0.14571 -0.196 0.84586
TREATMENTDSS+PEITC:Genotypenrf2KO 0.24747 0.14571 1.698 0.09721 .
TREATMENTDSS+Cranberry:Genotypenrf2KO 0.07927 0.14571 0.544 0.58946
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1785 on 40 degrees of freedom
Multiple R-squared: 0.249, Adjusted R-squared: 0.1176
F-statistic: 1.894 on 7 and 40 DF, p-value: 0.09608
# No significant interactions, proceed with 2-way analysis
m2 <- lm(delta ~ TREATMENT + Genotype,
data = dd)
summary(m2)
Call:
lm(formula = delta ~ TREATMENT + Genotype, data = dd)
Residuals:
Min 1Q Median 3Q Max
-0.49158 -0.09742 -0.01290 0.11101 0.35281
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.21414 0.05849 3.661 0.000683 ***
TREATMENTNaive -0.05851 0.07399 -0.791 0.433377
TREATMENTDSS+PEITC -0.03404 0.07399 -0.460 0.647813
TREATMENTDSS+Cranberry 0.08427 0.07399 1.139 0.261014
Genotypenrf2KO -0.10956 0.05232 -2.094 0.042176 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.1812 on 43 degrees of freedom
Multiple R-squared: 0.1674, Adjusted R-squared: 0.08999
F-statistic: 2.162 on 4 and 43 DF, p-value: 0.08947
At Week 8 there was significantly smaller increase of alpha diversity from baseline in Nrf2 KO compared to WT, and in DSS+Cranberry compared to DSS only.
Load aminoacids
aa <- fread("data_sep2019/sep2019_aminoacids.csv")
aa <- aa[!is.na(ID), ]
aa$ID <- paste0(aa$ID,
aa$CAGE)
smpl1 <- unique(smpl[, c("ID",
"TREATMENT",
"Genotype")])
smpl1$ID <- as.character(smpl1$ID)
aa <- merge(smpl1,
aa,
by = "ID")
aa[, trt_week := paste(TREATMENT,
WEEK,
sep = "_")]
aa$trt_week <- factor(aa$trt_week,
levels = c("Naive_week2",
"Naive_week6",
"DSS_week2",
"DSS_week6",
"DSS+Cranberry_week2",
"DSS+Cranberry_week6" ,
"DSS+PEITC_week2",
"DSS+PEITC_week6"))
Aminoacids by animal, treatment group and timepoint
for (i in 8:(ncol(aa) - 1)) {
tmp <- aa[, c(1, 3, 7, ncol(aa), i), with = FALSE]
colnames(tmp)[5] <- "Y"
p1 <- ggplot(tmp,
aes(x = trt_week,
y = Y,
fill = Genotype,
group = ID)) +
geom_line(position = position_dodge(0.3)) +
geom_point(shape = 21,
size = 3,
position = position_dodge(0.3)) +
scale_x_discrete("") +
scale_y_continuous(colnames(aa)[i]) +
theme(axis.text.x = element_text(angle = 45,
hjust = 1))
# tiff(filename = paste0("tmp/",
# colnames(aa)[i],
# ".tiff"),
# height = 4,
# width = 5,
# units = "in",
# res = 600,
# compression = "lzw+p")
# print(p1)
# graphics.off()
print(p1)
}




















Aminoacids over time, group averages
out <- list()
for (i in 8:27) {
tmp <- aa[, c(2, 3, 7, i),
with = FALSE]
names(tmp)[4] <- "val"
tmp[, mu := mean(val,
na.rm = TRUE),
by = list(TREATMENT,
Genotype,
WEEK)]
tmp[, sem := sd(val,
na.rm = TRUE)/sqrt(.N),
by = list(TREATMENT,
Genotype,
WEEK)]
out[[i - 7]] <- data.table(Aminoacid = names(aa)[i],
unique(tmp[, c("TREATMENT",
"Genotype",
"WEEK",
"mu",
"sem")]))
}
muaa <- rbindlist(out)
muaa$Aminoacid <- factor(muaa$Aminoacid,
levels = unique(muaa$Aminoacid))
muaa$Genotype <- factor(muaa$Genotype,
levels = c("widetype",
"nrf2KO"),
labels = c("Wild Type",
"Nrf2 KO"))
for (i in 1:nlevels(muaa$Aminoacid)) {
p1 <- ggplot(muaa[Aminoacid == levels(muaa$Aminoacid)[i], ],
aes(x = WEEK,
y = mu,
ymin = mu - sem,
ymax = mu + sem,
fill = TREATMENT,
group = TREATMENT)) +
facet_wrap(~ Genotype) +
geom_errorbar(position = position_dodge(0.4),
width = 0.4) +
geom_line(position = position_dodge(0.4)) +
geom_point(size = 3,
shape = 21,
position = position_dodge(0.4)) +
scale_x_discrete("",
breaks = c("week2",
"week6"),
labels = c("Week 2",
"Week 6")) +
scale_y_continuous(levels(muaa$Aminoacid)[i]) +
theme(axis.text.x = element_text(angle = 45,
hjust = 1),
legend.position = "top")
# tiff(filename = paste0("tmp/avg_",
# levels(muaa$Aminoacid)[i],
# ".tiff"),
# height = 5,
# width = 6,
# units = "in",
# res = 600,
# compression = "lzw+p")
# print(p1)
# graphics.off()
print(p1)
}




















Aminoacid data PCA
dt_pca <- aa[, Alanine:glutamine]
# m1 <- prcomp(dt_pca,
# center = TRUE,
# scale. = TRUE)
# m1 <- prcomp(dt_pca,
# center = FALSE,
# scale. = FALSE)
m1 <- prcomp(dt_pca)
summary(m1)
Importance of components:
PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8
Standard deviation 4.8108 2.5833 1.78182 1.13495 0.93367 0.84949 0.62035 0.49052
Proportion of Variance 0.6276 0.1810 0.08609 0.03493 0.02364 0.01957 0.01044 0.00652
Cumulative Proportion 0.6276 0.8086 0.89466 0.92959 0.95323 0.97280 0.98323 0.98976
PC9 PC10 PC11 PC12 PC13 PC14 PC15 PC16
Standard deviation 0.35960 0.26786 0.22940 0.20322 0.1486 0.13250 0.11932 0.11154
Proportion of Variance 0.00351 0.00195 0.00143 0.00112 0.0006 0.00048 0.00039 0.00034
Cumulative Proportion 0.99326 0.99521 0.99663 0.99775 0.9983 0.99883 0.99922 0.99955
PC17 PC18 PC19 PC20
Standard deviation 0.08896 0.07274 0.05110 0.02565
Proportion of Variance 0.00021 0.00014 0.00007 0.00002
Cumulative Proportion 0.99977 0.99991 0.99998 1.00000
# Select PC-s to pliot (PC1 & PC2)
choices <- 1:2
# Scores, i.e. points (df.u)
dt.scr <- data.table(m1$x[, choices])
# Add grouping variable
dt.scr$grp <- aa$trt_week
dt.scr$TREATMENT <- aa$TREATMENT
dt.scr$WEEK <- aa$WEEK
dt.scr
# Loadings, i.e. arrows (df.v)
dt.rot <- as.data.frame(m1$rotation[, choices])
dt.rot$feat <- rownames(dt.rot)
dt.rot <- data.table(dt.rot)
dt.rot
dt.load <- melt.data.table(dt.rot,
id.vars = "feat",
measure.vars = 1:2,
variable.name = "pc",
value.name = "loading")
dt.load$feat <- factor(dt.load$feat,
levels = unique(dt.load$feat))
# Plot loadings
p0 <- ggplot(data = dt.load,
aes(x = feat,
y = loading)) +
facet_wrap(~ pc,
nrow = 2) +
geom_bar(stat = "identity") +
ggtitle("PC Loadings") +
theme(plot.title = element_text(hjust = 0.5),
axis.text.x = element_text(angle = 45,
hjust = 1))
tiff(filename = "tmp/pc.1.2_loadings.tiff",
height = 5,
width = 8,
units = 'in',
res = 300,
compression = "lzw+p")
print(p0)
graphics.off()
print(p0)

# Axis labels
u.axis.labs <- paste(colnames(dt.rot)[1:2],
sprintf('(%0.1f%% explained var.)',
100*m1$sdev[choices]^2/sum(m1$sdev^2)))
u.axis.labs
[1] "PC1 (62.8% explained var.)" "PC2 (18.1% explained var.)"
# Based on Figure p0, keep only a few variables with high loadings in PC1 and PC2----
# var.keep.ndx <- which(dt.rot$feat %in% c(...))
# Or select all
# var.keep.ndx <- 3:ncol(dt1)
# Use dt.rot[var.keep.ndx,] and dt.rot$feat[var.keep.ndx]
p1 <- ggplot(data = dt.rot,
aes(x = PC1,
y = PC2)) +
coord_equal() +
geom_point(data = dt.scr,
aes(fill = grp),
shape = 21,
size = 2,
alpha = 0.5) +
geom_segment(aes(x = 0,
y = 0,
xend = 10*PC1,
yend = 10*PC2),
arrow = arrow(length = unit(1/2, 'picas')),
# size = 1,
color = "black") +
geom_text(aes(x = 11*PC1,
y = 11*PC2,
label = dt.rot$feat),
# size = 5,
hjust = 0.5) +
scale_x_continuous(u.axis.labs[1]) +
scale_y_continuous(u.axis.labs[2]) +
scale_fill_discrete(name = "Group") +
ggtitle("Biplot of Aminoacids") +
theme(plot.title = element_text(hjust = 0.5,
size = 20))
tiff(filename = "tmp/aminoacids_biplot.tiff",
height = 10,
width = 10,
units = 'in',
res = 300,
compression = "lzw+p")
print(p1)
graphics.off()
ggplotly(p1)
p2 <- ggplot(data = dt.rot,
aes(x = PC1,
y = PC2)) +
coord_equal() +
geom_point(data = dt.scr,
aes(fill = WEEK),
shape = 21,
size = 2,
alpha = 0.5) +
geom_segment(aes(x = 0,
y = 0,
xend = 10*PC1,
yend = 10*PC2),
arrow = arrow(length = unit(1/2, 'picas')),
size = 1.2,
color = "black") +
geom_text(aes(x = 11*PC1,
y = 11*PC2,
label = dt.rot$feat),
# size = 5,
hjust = 0.5) +
scale_x_continuous(u.axis.labs[1]) +
scale_y_continuous(u.axis.labs[2]) +
scale_fill_discrete(name = "Week") +
ggtitle("Biplot of Aminoacids") +
theme(plot.title = element_text(hjust = 0.5,
size = 20))
tiff(filename = "tmp/aminoacids_by_week_biplot.tiff",
height = 10,
width = 10,
units = 'in',
res = 300,
compression = "lzw+p")
print(p2)
graphics.off()
ggplotly(p2)
p2 <- ggplot(data = dt.rot,
aes(x = PC1,
y = PC2)) +
coord_equal() +
geom_point(data = dt.scr,
aes(fill = TREATMENT),
shape = 21,
size = 2,
alpha = 0.5) +
geom_segment(aes(x = 0,
y = 0,
xend = 10*PC1,
yend = 10*PC2),
arrow = arrow(length = unit(1/2, 'picas')),
size = 1.2,
color = "black") +
geom_text(aes(x = 11*PC1,
y = 11*PC2,
label = dt.rot$feat),
# size = 5,
hjust = 0.5) +
scale_x_continuous(u.axis.labs[1]) +
scale_y_continuous(u.axis.labs[2]) +
scale_fill_discrete(name = "Treatment") +
ggtitle("Biplot of Aminoacids") +
theme(plot.title = element_text(hjust = 0.5,
size = 20))
tiff(filename = "tmp/aminoacids_by_trt_biplot.tiff",
height = 10,
width = 10,
units = 'in',
res = 300,
compression = "lzw+p")
print(p2)
graphics.off()
ggplotly(p2)
Remove unmapped OTUs
The 311 unmapped OTUs were removed from further analysis (with 7,247 OTUs left).
ps1 <- subset_taxa(ps0,
!is.na(Phylum))
dim(ps1@otu_table@.Data)
[1] 72 7247
Relative abundance (%) at Phylum level
Remove phyla with relative abundance of >= 1% in less than 10% of samples.
t1 <- data.table(Phylum = ra_p$Phylum,
`Number of Samples` = rowSums(ra_p[, 2:ncol(ra_p)] >= 0.01))
t1$`Percent Samples` <- t1$`Number of Samples`/72
setorder(t1, -`Number of Samples`)
datatable(t1,
rownames = FALSE,
caption = "Taxonomic count table",
class = "cell-border stripe",
options = list(search = FALSE,
pageLength = nrow(t1))) %>%
formatPercentage(columns = 3,
digits = 1)
We will remove Chlamydiae from this analysis.
[1] "Firmicutes, Bacteroidetes, Proteobacteria, Verrucomicrobia, Epsilonbacteraeota, Actinobacteria, Deferribacteres, Patescibacteria, Cyanobacteria"
7,224 OTUs, down from 7,247 OTUs in the previous table.
Relative Abundance in Samples at Different Taxonomic Ranks
PCA at Class level
dt_pca <- t(ra_c[, 3:ncol(ra_c)])
colnames(dt_pca) <- ra_c$Class
dt_pca_c <- data.table(SAMPLE_NAME = rownames(dt_pca),
dt_pca)
dt_pca_c <- merge(smpl,
dt_pca_c,
by = "SAMPLE_NAME")
# m1 <- prcomp(dt_pca,
# center = TRUE,
# scale. = TRUE)
# m1 <- prcomp(dt_pca,
# center = FALSE,
# scale. = FALSE)
m1 <- prcomp(dt_pca)
summary(m1)
Importance of components:
PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8
Standard deviation 14.6908 8.5044 7.5927 5.76824 3.41220 2.73038 2.43631 1.46850
Proportion of Variance 0.5294 0.1774 0.1414 0.08162 0.02856 0.01829 0.01456 0.00529
Cumulative Proportion 0.5294 0.7068 0.8482 0.92987 0.95844 0.97672 0.99128 0.99657
PC9 PC10 PC11 PC12 PC13 PC14
Standard deviation 0.93207 0.55762 0.43780 0.15826 0.02399 8.187e-17
Proportion of Variance 0.00213 0.00076 0.00047 0.00006 0.00000 0.000e+00
Cumulative Proportion 0.99870 0.99947 0.99994 1.00000 1.00000 1.000e+00
# Select PC-s to pliot (PC1 & PC2)
choices <- 1:2
# Scores, i.e. points (df.u)
dt.scr <- data.table(m1$x[, choices])
# Add grouping variable
dt.scr$grp <- paste(dt_pca_c$TREATMENT,
dt_pca_c$WEEK,
dt_pca_c$Genotype)
dt.scr$TREATMENT <- dt_pca_c$TREATMENT
dt.scr$WEEK <- dt_pca_c$WEEK
dt.scr$Genotype <- dt_pca_c$Genotype
dt.scr
# Loadings, i.e. arrows (df.v)
dt.rot <- as.data.frame(m1$rotation[, choices])
dt.rot$feat <- rownames(dt.rot)
dt.rot <- data.table(dt.rot)
dt.rot
dt.load <- melt.data.table(dt.rot,
id.vars = "feat",
measure.vars = 1:2,
variable.name = "pc",
value.name = "loading")
dt.load$feat <- factor(dt.load$feat,
levels = unique(dt.load$feat))
# Plot loadings
p0 <- ggplot(data = dt.load,
aes(x = feat,
y = loading)) +
facet_wrap(~ pc,
nrow = 2) +
geom_bar(stat = "identity") +
ggtitle("PC Loadings") +
theme(plot.title = element_text(hjust = 0.5),
axis.text.x = element_text(angle = 45,
hjust = 1))
tiff(filename = "tmp/pc.1.2_loadings_class.tiff",
height = 5,
width = 8,
units = 'in',
res = 300,
compression = "lzw+p")
print(p0)
graphics.off()
print(p0)

# Axis labels
u.axis.labs <- paste(colnames(dt.rot)[1:2],
sprintf('(%0.1f%% explained var.)',
100*m1$sdev[choices]^2/sum(m1$sdev^2)))
u.axis.labs
[1] "PC1 (52.9% explained var.)" "PC2 (17.7% explained var.)"
# Based on Figure p0, keep only a few variables with high loadings in PC1 and PC2----
# var.keep.ndx <- which(dt.rot$feat %in% c(...))
# Or select all
# var.keep.ndx <- 3:ncol(dt1)
# Use dt.rot[var.keep.ndx,] and dt.rot$feat[var.keep.ndx]
p1 <- ggplot(data = dt.rot,
aes(x = PC1,
y = PC2)) +
coord_equal() +
geom_point(data = dt.scr,
aes(fill = grp),
shape = 21,
size = 2,
alpha = 0.5) +
geom_segment(aes(x = 0,
y = 0,
xend = 40*PC1,
yend = 40*PC2),
arrow = arrow(length = unit(1/2, 'picas')),
# size = 1,
color = "black") +
geom_text(aes(x = 44*PC1,
y = 44*PC2,
label = dt.rot$feat),
# size = 5,
hjust = 0.5) +
scale_x_continuous(u.axis.labs[1]) +
scale_y_continuous(u.axis.labs[2]) +
scale_fill_discrete(name = "Group") +
ggtitle("Biplot of Classes of Bacteria") +
theme(plot.title = element_text(hjust = 0.5,
size = 20))
tiff(filename = "tmp/class_biplot_grp.tiff",
height = 10,
width = 10,
units = 'in',
res = 300,
compression = "lzw+p")
print(p1)
graphics.off()
ggplotly(p1)
# Find centers of each group
grpg <- "grp"
var1 <- eval(parse(text = paste("dt.scr$",
grpg,
sep = "")))
cntr <- data.table(Group = unique(var1),
PC1 = aggregate(x = dt.scr$PC1,
by = list(var1),
FUN = "mean")$x,
PC2 = aggregate(x = dt.scr$PC2,
by = list(var1),
FUN = "mean")$x)
p2 <- p1 + geom_label(data = cntr,
aes(x = PC1,
y = PC2,
label = Group,
colour = Group),
alpha = 0.5,
size = 3) +
scale_color_discrete(guide = FALSE) +
theme(legend.position = "none")
print(p2)

# Based on Figure p0, keep only a few variables with high loadings in PC1 and PC2----
# var.keep.ndx <- which(dt.rot$feat %in% c(...))
# Or select all
# var.keep.ndx <- 3:ncol(dt1)
# Use dt.rot[var.keep.ndx,] and dt.rot$feat[var.keep.ndx]
p1 <- ggplot(data = dt.rot,
aes(x = PC1,
y = PC2)) +
coord_equal() +
geom_point(data = dt.scr,
aes(fill = Genotype),
shape = 21,
size = 2,
alpha = 0.5) +
geom_segment(aes(x = 0,
y = 0,
xend = 40*PC1,
yend = 40*PC2),
arrow = arrow(length = unit(1/2, 'picas')),
# size = 1,
color = "black") +
geom_text(aes(x = 44*PC1,
y = 44*PC2,
label = dt.rot$feat),
# size = 5,
hjust = 0.5) +
scale_x_continuous(u.axis.labs[1]) +
scale_y_continuous(u.axis.labs[2]) +
scale_fill_discrete(name = "Group") +
ggtitle("Biplot of Classes of Bacteria") +
theme(plot.title = element_text(hjust = 0.5,
size = 20))
tiff(filename = "tmp/class_biplot_genotype.tiff",
height = 10,
width = 10,
units = 'in',
res = 300,
compression = "lzw+p")
print(p1)
graphics.off()
ggplotly(p1)
# Find centers of each group
grpg <- "Genotype"
var1 <- eval(parse(text = paste("dt.scr$",
grpg,
sep = "")))
cntr <- data.table(Group = unique(var1),
PC1 = aggregate(x = dt.scr$PC1,
by = list(var1),
FUN = "mean")$x,
PC2 = aggregate(x = dt.scr$PC2,
by = list(var1),
FUN = "mean")$x)
p2 <- p1 + geom_label(data = cntr,
aes(x = PC1,
y = PC2,
label = Group,
colour = Group),
alpha = 0.5,
size = 3) +
scale_color_discrete(guide = FALSE) +
theme(legend.position = "none")
print(p2)

# Based on Figure p0, keep only a few variables with high loadings in PC1 and PC2----
# var.keep.ndx <- which(dt.rot$feat %in% c(...))
# Or select all
# var.keep.ndx <- 3:ncol(dt1)
# Use dt.rot[var.keep.ndx,] and dt.rot$feat[var.keep.ndx]
p1 <- ggplot(data = dt.rot,
aes(x = PC1,
y = PC2)) +
coord_equal() +
geom_point(data = dt.scr,
aes(fill = WEEK),
shape = 21,
size = 2,
alpha = 0.5) +
geom_segment(aes(x = 0,
y = 0,
xend = 40*PC1,
yend = 40*PC2),
arrow = arrow(length = unit(1/2, 'picas')),
# size = 1,
color = "black") +
geom_text(aes(x = 44*PC1,
y = 44*PC2,
label = dt.rot$feat),
# size = 5,
hjust = 0.5) +
scale_x_continuous(u.axis.labs[1]) +
scale_y_continuous(u.axis.labs[2]) +
scale_fill_discrete(name = "Group") +
ggtitle("Biplot of Classes of Bacteria") +
theme(plot.title = element_text(hjust = 0.5,
size = 20))
tiff(filename = "tmp/class_biplot_week.tiff",
height = 10,
width = 10,
units = 'in',
res = 300,
compression = "lzw+p")
print(p1)
graphics.off()
ggplotly(p1)
# Find centers of each group
grpg <- "WEEK"
var1 <- eval(parse(text = paste("dt.scr$",
grpg,
sep = "")))
cntr <- data.table(Group = unique(var1),
PC1 = aggregate(x = dt.scr$PC1,
by = list(var1),
FUN = "mean")$x,
PC2 = aggregate(x = dt.scr$PC2,
by = list(var1),
FUN = "mean")$x)
p2 <- p1 + geom_label(data = cntr,
aes(x = PC1,
y = PC2,
label = Group,
colour = Group),
alpha = 0.5,
size = 3) +
scale_color_discrete(guide = FALSE) +
theme(legend.position = "none")
print(p2)

# Based on Figure p0, keep only a few variables with high loadings in PC1 and PC2----
# var.keep.ndx <- which(dt.rot$feat %in% c(...))
# Or select all
# var.keep.ndx <- 3:ncol(dt1)
# Use dt.rot[var.keep.ndx,] and dt.rot$feat[var.keep.ndx]
p1 <- ggplot(data = dt.rot,
aes(x = PC1,
y = PC2)) +
coord_equal() +
geom_point(data = dt.scr,
aes(fill = TREATMENT),
shape = 21,
size = 3,
alpha = 0.5) +
geom_segment(aes(x = 0,
y = 0,
xend = 40*PC1,
yend = 40*PC2),
arrow = arrow(length = unit(1/2, 'picas')),
# size = 1,
color = "black") +
geom_text(aes(x = 44*PC1,
y = 44*PC2,
label = dt.rot$feat),
# size = 5,
hjust = 0.5) +
scale_x_continuous(u.axis.labs[1]) +
scale_y_continuous(u.axis.labs[2]) +
scale_fill_manual(name = "Treatment",
breaks = levels(dt.scr$TREATMENT),
values = c("red",
"green",
"orange",
"blue")) +
ggtitle("Biplot of Classes of Bacteria") +
theme(plot.title = element_text(hjust = 0.5,
size = 20))
ggplotly(p1)
# Find centers of each group
# grpg <- "TREATMENT"
# var1 <- eval(parse(text = paste("dt.scr$",
# grpg,
# sep = "")))
# cntr <- data.table(Group = levels(var1),
# PC1 = aggregate(x = dt.scr$PC1,
# by = list(var1),
# FUN = "mean")$x,
# PC2 = aggregate(x = dt.scr$PC2,
# by = list(var1),
# FUN = "mean")$x)
cntr <- data.table(PC1 = aggregate(x = dt.scr$PC1,
by = list(dt.scr$TREATMENT),
FUN = "mean"),
PC2 = aggregate(x = dt.scr$PC2,
by = list(var1),
FUN = "mean")$x)
colnames(cntr) <- c("TREATMENT",
"PC1",
"PC2")
p2 <- p1 + geom_label(data = cntr,
aes(x = PC1,
y = PC2,
label = TREATMENT,
colour = TREATMENT),
alpha = 0.5,
size = 3) +
scale_color_manual(guide = FALSE,
breaks = levels(cntr$TREATMENT),
values = c("red",
"green",
"orange",
"blue")) +
theme(legend.position = "none")
tiff(filename = "tmp/class_biplot_trt.tiff",
height = 8,
width = 8,
units = 'in',
res = 300,
compression = "lzw+p")
print(p2)
graphics.off()
print(p2)

# Covariates only
m1 <- multinom(TREATMENT ~ WEEK + Genotype,
data = dt.scr)
# weights: 20 (12 variable)
initial value 99.813194
final value 99.813194
converged
summary(m1)
Call:
multinom(formula = TREATMENT ~ WEEK + Genotype, data = dt.scr)
Coefficients:
(Intercept) WEEKweek1 WEEKweek8 Genotypewidetype
Naive 0 0 0 0
DSS+PEITC 0 0 0 0
DSS+Cranberry 0 0 0 0
Std. Errors:
(Intercept) WEEKweek1 WEEKweek8 Genotypewidetype
Naive 0.6666667 0.8164966 0.8164966 0.6666667
DSS+PEITC 0.6666667 0.8164966 0.8164966 0.6666667
DSS+Cranberry 0.6666667 0.8164966 0.8164966 0.6666667
Residual Deviance: 199.6264
AIC: 223.6264
prd1 <- predict(m1)
t1 <- table(Predicted = prd1,
Observed = dt.scr$TREATMENT)
# PC1 alone
m2 <- multinom(TREATMENT ~ PC1,
data = dt.scr)
# weights: 12 (6 variable)
initial value 99.813194
iter 10 value 84.652280
final value 84.650617
converged
summary(m2)
Call:
multinom(formula = TREATMENT ~ PC1, data = dt.scr)
Coefficients:
(Intercept) PC1
Naive -0.84291965 -0.13135533
DSS+PEITC 0.02563488 -0.01778110
DSS+Cranberry -0.28377674 0.04267039
Std. Errors:
(Intercept) PC1
Naive 0.5329226 0.03966130
DSS+PEITC 0.3375675 0.02567473
DSS+Cranberry 0.3902129 0.02668075
Residual Deviance: 169.3012
AIC: 181.3012
prd2 <- predict(m2)
t2 <- table(Predicted = prd2,
Observed = dt.scr$TREATMENT)
# PC1 with covariates
m3 <- multinom(TREATMENT ~ PC1 + WEEK + Genotype,
data = dt.scr)
# weights: 24 (15 variable)
initial value 99.813194
iter 10 value 78.199054
final value 77.966110
converged
summary(m3)
Call:
multinom(formula = TREATMENT ~ PC1 + WEEK + Genotype, data = dt.scr)
Coefficients:
(Intercept) PC1 WEEKweek1 WEEKweek8 Genotypewidetype
Naive -1.69181404 -0.16561286 -0.19417414 -0.2467395 2.0439498
DSS+PEITC -0.22775882 -0.03756158 -0.03426154 -0.1624952 0.6998366
DSS+Cranberry 0.04994931 0.11984007 0.18496264 0.6354061 -2.2783047
Std. Errors:
(Intercept) PC1 WEEKweek1 WEEKweek8 Genotypewidetype
Naive 0.9208565 0.04798080 1.0184358 0.9866132 1.0821427
DSS+PEITC 0.7006164 0.03813898 0.8248932 0.8419015 0.9838589
DSS+Cranberry 0.7578087 0.04894181 0.8842508 0.9154535 1.1724461
Residual Deviance: 155.9322
AIC: 185.9322
prd3 <- predict(m3)
t3 <- table(Predicted = prd3,
Observed = dt.scr$TREATMENT)
# PC1 + PC2 with covariates
m4 <- multinom(TREATMENT ~ PC1 + PC2 + WEEK + Genotype,
data = dt.scr)
# weights: 28 (18 variable)
initial value 99.813194
iter 10 value 77.643401
iter 20 value 76.157664
final value 76.157652
converged
summary(m4)
Call:
multinom(formula = TREATMENT ~ PC1 + PC2 + WEEK + Genotype, data = dt.scr)
Coefficients:
(Intercept) PC1 PC2 WEEKweek1 WEEKweek8 Genotypewidetype
Naive -1.6723699 -0.16055402 0.02692425 0.0603261 -0.04440483 1.7116558
DSS+PEITC -0.1610640 -0.03200954 -0.04731926 -0.3059877 -0.35846181 0.6645649
DSS+Cranberry -0.1487237 0.11347301 0.04018812 0.3641810 0.72081476 -2.0106669
Std. Errors:
(Intercept) PC1 PC2 WEEKweek1 WEEKweek8 Genotypewidetype
Naive 0.9329651 0.04827333 0.04795722 1.1064244 1.0330649 1.083235
DSS+PEITC 0.7176961 0.03961699 0.04860606 0.8745219 0.8762840 1.022935
DSS+Cranberry 0.8377326 0.04936837 0.06510574 0.9608816 0.9434467 1.204373
Residual Deviance: 152.3153
AIC: 188.3153
prd4 <- predict(m4)
t4 <- table(Predicted = prd4,
Observed = dt.scr$TREATMENT)
# Confusion tables
datatable(cbind(t1),
caption = "Covariates Only")
datatable(cbind(t2),
caption = "PC1 Only")
datatable(cbind(t3),
caption = "PC1 with covariates")
datatable(cbind(t4),
caption = "PC1 + PC2 with covariates")
# Compare models
anova(m1, m3)
Likelihood ratio tests of Multinomial Models
Response: TREATMENT
Model Resid. df Resid. Dev Test Df LR stat. Pr(Chi)
1 WEEK + Genotype 204 199.6264
2 PC1 + WEEK + Genotype 201 155.9322 1 vs 2 3 43.69417 1.752679e-09
anova(m2, m3)
Likelihood ratio tests of Multinomial Models
Response: TREATMENT
Model Resid. df Resid. Dev Test Df LR stat. Pr(Chi)
1 PC1 210 169.3012
2 PC1 + WEEK + Genotype 201 155.9322 1 vs 2 9 13.36901 0.1466072
anova(m4, m3)
Likelihood ratio tests of Multinomial Models
Response: TREATMENT
Model Resid. df Resid. Dev Test Df LR stat. Pr(Chi)
1 PC1 + WEEK + Genotype 201 155.9322
2 PC1 + PC2 + WEEK + Genotype 198 152.3153 1 vs 2 3 3.616917 0.305912
The results suggest that:
a. Covariates alone (timepoint and genotype) cannot explain the difference between treatments.
b. Principal Component 1 (PC1) can explain the differences in relative abundance of classes in the samples. The model does not improve significantly by adding the covariates or the PC2. However, the covariates should stay in the model for adjustment, and PC2 slightly improves the predictions.
c. The full model (PC1 + PC2 + Week + Genotype) correctly classifies 12 out of 18 Naive samples, and 12 out of 18 DSS+Cranburry samples.
Continuing the same analysis at Order, Family and Genus levels.
DO ROC AUC NEST!!!
# # Output probobilities----
# prd1.1 <- predict(m1,
# type = "probs")
# prd1.1 <- data.table(ID = df.u$ID,
# Treatment = df.u$Treatment,
# round(prd1.1,
# 4))
#
# prd2.1 <- predict(m2,
# type = "probs")
# prd2.1 <- data.table(ID = df.u$ID,
# Treatment = df.u$Treatment,
# round(prd2.1,
# 4))
#
# # Sensitivity/Specificity
# # Tresholds
# trhd <- seq(0, 1, by = 0.01)
#
# out1 <- list()
# for (i in 1:length(trhd)) {
# tmp <- apply(prd1.1[, -c(1:2)],
# MARGIN = 2,
# FUN = function(a) {
# return(a >= trhd[i])
# })
# tmp2 <- apply(X = tmp,
# MARGIN = 2,
# FUN = function(a) {
# aggregate(x = a,
# by = list(prd1.1$Treatment),
# FUN = sum)$x
# })
# tmp2
# out1[[i]] <- c(sens = sum(diag(tmp2))/nrow(prd1.1),
# spec = (sum(tmp2[upper.tri(tmp2)]) +
# sum(tmp2[lower.tri(tmp2)]))/(nrow(tmp)*(ncol(tmp) - 1)))
# }
# out1 <- data.table(do.call("rbind", out1))
# out1 <- unique(out1)
#
# out2 <- list()
# for (i in 1:length(trhd)) {
# tmp <- apply(prd2.1[, -c(1:2)],
# MARGIN = 2,
# FUN = function(a) {
# return(a >= trhd[i])
# })
# tmp2 <- apply(X = tmp,
# MARGIN = 2,
# FUN = function(a) {
# aggregate(x = a,
# by = list(prd2.1$Treatment),
# FUN = sum)$x
# })
# out2[[i]] <- c(sens = sum(diag(tmp2))/nrow(prd2.1),
# spec = (sum(tmp2[upper.tri(tmp2)]) +
# sum(tmp2[lower.tri(tmp2)]))/(nrow(tmp)*(ncol(tmp) - 1)))
# }
# out2 <- data.table(do.call("rbind", out2))
# out2 <- unique(out2)
#
# # ROC
# roc1 <- auc(x = out1$spec,
# y = out1$sens,
# from = 0,
# to = 1)
#
# roc2 <- auc(x = out2$spec,
# y = out2$sens,
# from = 0,
# to = 1)
#
# # ROC plot
# plot(out1$sens ~ out1$spec,
# type = "l",
# xlim = c(0, 1),
# ylim = c(0, 1),
# xlab = "1 - Specificity",
# ylab = "Sensitivity",
# col = "blue")
# lines(out2$sens ~ out2$spec,
# col = "red")
# text(x = c(0.8, 0.8),
# y = c(0.2, 0.3),
# label = c(paste("ROC(PC1) = ",
# round(roc1,
# 3)),
# paste("\nROC(PC1+PC2) = ",
# round(roc2,
# 3))),
# col = c("blue",
# "red"))
# abline(0, 1, lty = 2)
mu$Trt_Genotype <- factor(paste(mu$Treatment,
mu$Genotype,
sep = "_"))
p0 <- ggplot(mu,
aes(x = Week,
y = x,
group = Trt_Genotype)) +
facet_wrap(~ Class,
scale = "free_y") +
geom_line(position = position_dodge(0.3)) +
geom_point(aes(fill = Trt_Genotype),
shape = 21,
size = 2,
alpha = 0.5,
position = position_dodge(0.3)) +
scale_x_discrete("") +
scale_y_continuous("Relative Abundance (%)") +
theme(legend.position = "top",
axis.text.x = element_text(angle = 45,
hjust = 1))
tiff(filename = "tmp/wt_class_over_time.tiff",
height = 5,
width = 7,
units = "in",
res = 600,
compression = "lzw+p")
print(p0)
graphics.off()
print(p0)

p1 <- ggplot(mu,
aes(x = x,
y = Class,
color = Trt_Genotype,
shape = Week)) +
geom_point(size = 3,
alpha = 0.5) +
geom_vline(xintercept = 1,
linetype = "dashed") +
scale_x_continuous("Relative Abundance (%)")
tiff(filename = "tmp/wt_class_ra.tiff",
height = 4,
width = 7,
units = "in",
res = 600,
compression = "lzw+p")
print(p1)
graphics.off()
ggplotly(p1+
theme(legend.position = "none"))
2. Order
mu$Trt_Genotype <- factor(paste(mu$Treatment,
mu$Genotype,
sep = "_"))
p0 <- ggplot(mu,
aes(x = Week,
y = x,
group = Trt_Genotype)) +
facet_wrap(~ Order,
scale = "free_y") +
geom_line(position = position_dodge(0.3)) +
geom_point(aes(fill = Trt_Genotype),
shape = 21,
size = 2,
alpha = 0.5,
position = position_dodge(0.3)) +
scale_x_discrete("") +
scale_y_continuous("Relative Abundance (%)") +
theme(legend.position = "top",
axis.text.x = element_text(angle = 45,
hjust = 1))
tiff(filename = "tmp/wt_Order_over_time.tiff",
height = 5,
width = 7,
units = "in",
res = 600,
compression = "lzw+p")
print(p0)
graphics.off()
print(p0)
p1 <- ggplot(mu,
aes(x = x,
y = Order,
color = Trt_Genotype,
shape = Week)) +
geom_point(size = 3,
alpha = 0.5) +
geom_vline(xintercept = 1,
linetype = "dashed") +
scale_x_continuous("Relative Abundance (%)")
tiff(filename = "tmp/wt_Order_ra.tiff",
height = 4,
width = 7,
units = "in",
res = 600,
compression = "lzw+p")
print(p1)
graphics.off()
ggplotly(p1 +
theme(legend.position = "none"))
3. Family
NOTE: only the first 24 families had large enough counts - ploting only them.
mu$Trt_Genotype <- factor(paste(mu$Treatment,
mu$Genotype,
sep = "_"))
mu1 <- droplevels(mu[Family %in% levels(mu$Family)[nlevels(mu$Family):(nlevels(mu$Family) - 24)], ])
p0 <- ggplot(mu1,
aes(x = Week,
y = x,
group = Trt_Genotype)) +
facet_wrap(~ Family,
scale = "free_y") +
geom_line(position = position_dodge(0.3)) +
geom_point(aes(fill = Trt_Genotype),
shape = 21,
size = 2,
alpha = 0.5,
position = position_dodge(0.3)) +
scale_x_discrete("") +
scale_y_continuous("Relative Abundance (%)") +
theme(legend.position = "top",
axis.text.x = element_text(angle = 45,
hjust = 1))
tiff(filename = "tmp/wt_Family_over_time.tiff",
height = 7,
width = 9,
units = "in",
res = 600,
compression = "lzw+p")
print(p0)
graphics.off()
print(p0)
p1 <- ggplot(mu1,
aes(x = x,
y = Family,
color = Trt_Genotype,
shape = Week)) +
geom_point(size = 3,
alpha = 0.5) +
geom_vline(xintercept = 1,
linetype = "dashed") +
scale_x_continuous("Relative Abundance (%)") +
theme(legend.position = "top")
tiff(filename = "tmp/wt_Family_ra.tiff",
height = 4,
width = 7,
units = "in",
res = 600,
compression = "lzw+p")
print(p1)
graphics.off()
ggplotly(p1+
theme(legend.position = "none"))
4. Genus
mu$Trt_Genotype <- factor(paste(mu$Treatment,
mu$Genotype,
sep = "_"))
mu1 <- droplevels(mu[Genus %in% levels(mu$Genus)[nlevels(mu$Genus):(nlevels(mu$Genus) - 35)], ])
p0 <- ggplot(mu1,
aes(x = Week,
y = x,
group = Trt_Genotype)) +
facet_wrap(~ Genus,
scale = "free_y") +
geom_line(position = position_dodge(0.3)) +
geom_point(aes(fill = Trt_Genotype),
shape = 21,
size = 2,
alpha = 0.5,
position = position_dodge(0.3)) +
scale_x_discrete("") +
scale_y_continuous("Relative Abundance (%)") +
theme(legend.position = "top",
axis.text.x = element_text(angle = 45,
hjust = 1))
tiff(filename = "tmp/wt_Genus_over_time.tiff",
height = 9,
width = 12,
units = "in",
res = 600,
compression = "lzw+p")
print(p0)
graphics.off()
print(p0+
theme(legend.position = "none"))
p1 <- ggplot(mu1,
aes(x = x,
y = Genus,
color = Trt_Genotype,
shape = Week)) +
geom_point(size = 3,
alpha = 0.5) +
geom_vline(xintercept = 1,
linetype = "dashed") +
scale_x_continuous("Relative Abundance (%)") +
theme(legend.position = "top")
tiff(filename = "tmp/wt_Genus_ra.tiff",
height = 9,
width = 9,
units = "in",
res = 600,
compression = "lzw+p")
print(p1)
graphics.off()
ggplotly(p1+
theme(legend.position = "none"))
---
title: "Data Visualization of WT and Nrf2 KO (-/-) BL6 PEITC or Cranberry Treated Mice 16S Microbiome Data Analysis, September 2019 Batch"
output: 
  html_notebook:
    toc: yes
    toc_float: yes
    number_sections: yes
    code_folding: hide
---
Date: `r date()`     
Scientist: [Ran Yin](mailto:ry147@scarletmail.rutgers.edu)      
Sequencing (Waksman): [Dibyendu Kumar](mailto:dk@waksman.rutgers.edu)      
Statistics: [Davit Sargsyan](mailto:sargdavid@gmail.com)      
Principal Investigator: [Ah-Ng Kong](mailto:kongt@pharmacy.rutgers.edu) 

```{}
# Taxonomic Ranks:
# **K**ing **P**hillip **C**an n**O**t **F**ind **G**reen **S**ocks
# * Kingdom                
# * Phylum                    
# * Class                   
# * Order                   
# * Family     
# * Genus     
# * Species  
```

```{r setup}
# options(stringsAsFactors = FALSE,
#         scipen = 999)

# # Increase mmemory size to 64 Gb----
# invisible(utils::memory.limit(65536))


# str(knitr::opts_chunk$get())
# # NOTE: the below does not work!
# knitr::opts_chunk$set(echo = FALSE, 
#                       message = FALSE,
#                       warning = FALSE,
#                       error = FALSE)

# require(knitr)
# require(kableExtra)
# require(shiny)

require(phyloseq)
require(data.table)
require(ggplot2)
require(plotly)
require(DT)
require(lmerTest)
require(nnet)

source("source/functions_may2019.R")

# On Windows set multithread=FALSE----
mt <- TRUE
```

# Introduction
C57BL/6 wild-type (WT) and Nrf-2 double-knock-out (KO -/-) mice were given 2-week microbiome stabilization process using AIN93M diet and 8 more weeks to treat with either AIN93M or AIN93M 5% PEITC diet. Fecal samples were collected weekly, immediately frozen in liquid nitrogen and stored at -80^o^C. Serum, cecal, colon epithelial and whole colon tissues at week 10 were also collected for further analyses. Baseline, week 1 and 4 fecal samples were selected for 16s rRNA sequencing.  
  
This document examines results from the WT mice samples.  
  
We will attampt to answer the following questions:  
1. Did microbiome change over time?  
2. Was microbiome affected by diet?  
3. Was there a difference between the KO and WT?  
4. If there was a change in microbiome composition, what functional changes did it carry? What are the essential functions of the bacteria affected by the treatment and how can this be shown in vivo (metabolites, inflammation markers, etc.)?

# Data preprocessing
## Raw Data 
FastQ files were downloaded from [this Rutgers Box location](https://rutgers.app.box.com/folder/90143462291). A total of 144 files (2 per sample, pair-ended) and a pair of undetermined reads were downloaded. 

## Script
This script (***nrf2ubiome_dada2_sep2019_v1.Rmd***) was developed using [DADA2 Pipeline Tutorial (1.12)](https://benjjneb.github.io/dada2/tutorial.html) with tips and tricks from the [University of Maryland Shool of Medicine Institute for Genome Sciences (IGS)](http://www.igs.umaryland.edu/) [Microbiome Analysis Workshop (April 8-11, 2019)](http://www.igs.umaryland.edu/education/wkshp_metagenome.php). The output of the DADA2 script (***data_may2019/ps_sep2019.RData***) is explored in this document.

# Meta data: sample description
```{r data}
# Load data----
# Counts
load("data_sep2019/ps_sep2019.RData")

# Taxonomy
load("data_sep2019/taxa.RData")
taxa <- data.table(seq16s = rownames(taxa),
                   taxa)
```

**NOTE: correction to the meta-data!** (11/15/2019)
```{r correct_meta_data}
correct_samples <- fread("data_sep2019/16s metadata Sep-2019.csv")
ps_sep2019@sam_data$DSS <- correct_samples$DSS
```

# Samples
```{r samples}
ps_sep2019@sam_data$Genotype_Week <- paste(ps_sep2019@sam_data$genotype,
                                           ps_sep2019@sam_data$time,
                                           sep = "_")
ps_sep2019@sam_data$ID <- factor(paste0(ps_sep2019@sam_data$mice_num,
                                        ps_sep2019@sam_data$cage))

ps_sep2019@sam_data$TREATMENT <- paste0(ps_sep2019@sam_data$DSS,
                                        ps_sep2019@sam_data$PEITC,
                                        ps_sep2019@sam_data$cranberry)
ps_sep2019@sam_data$TREATMENT <- factor(ps_sep2019@sam_data$TREATMENT,
                                        levels = c("000",
                                                   "100",
                                                   "110",
                                                   "101"),
                                        labels = c("Naive",
                                                   "DSS",
                                                   "DSS+PEITC",
                                                   "DSS+Cranberry"))

samples <- ps_sep2019@sam_data
datatable(samples,
          options = list(pageLength = nrow(samples)))
```

# Prune data
The OTUs were mapped to Bacteria (96.07%), Eukaryota (2.95%) and Archea (0.03%) kingdoms, and  75 OTUs (0.95%) undefined. 

```{r check_mapping_kingdom, warning = FALSE, echo = FALSE, message = FALSE}
t1 <- data.table(table(tax_table(ps_sep2019)[, "Kingdom"],
                       exclude = NULL))
t1$V1[is.na(t1$V1)] <- "Unknown"

t1[, pct := N/sum(N)]
setorder(t1, -N)

colnames(t1) <- c("Kingdom",
                  "Number of OTUs",
                  "Percent of OTUs")
datatable(t1,
          rownames = FALSE,
          caption = "Number of OTUs by Kingdom",
          class = "cell-border stripe",
          options = list(search = FALSE,
                         pageLength = nrow(t1))) %>%
  formatCurrency(columns = 2,
                 currency = "",
                 mark = ",",
                 digits = 0) %>%
  formatPercentage(columns = 3,
                   digits = 2)
```

The total of 7,867 unique sequences were found. Out of those, 7,558 were mapped to bacterial genomes. 

```{r keep_bacteria}
dim(ps_sep2019@otu_table@.Data)

# Remove OTU not mapped to Bacteria
ps0 <- subset_taxa(ps_sep2019, 
                   Kingdom == "Bacteria")
dim(ps0@otu_table@.Data)
```
  
Out of the 7,558 OTUs 7,247 belonged to 12 Phyla. 311 of the OTUs (or 4.11% of bacterial OTUs) could not be mapped to a phylum.

```{r phylum_mapping}
t2 <- data.table(table(tax_table(ps0)[, "Phylum"],
                                  exclude = NULL))
t2$V1[is.na(t2$V1)] <- "Unknown"
setorder(t2, -N)
t2[, pct := N/sum(N)]
setorder(t2, -N)

colnames(t2) <- c("Phylum",
                  "Number of OTUs",
                  "Percent of OTUs")

datatable(t2,
          rownames = FALSE,
          caption = "Number of Bacterial OTUs by Phylum",
          class = "cell-border stripe",
          options = list(search = FALSE,
                         pageLength = nrow(t2))) %>%
  formatCurrency(columns = 2,
                 currency = "",
                 mark = ",",
                 digits = 0) %>%
  formatPercentage(columns = 3,
                   digits = 2)
```

# OTU table (first 10 rows)
```{r otu_table, warning=FALSE,echo=FALSE,message=FALSE}
otu <- data.table(ps0@tax_table@.Data,
                  t(ps0@otu_table@.Data))
datatable(head(otu, 10),
          rownames = FALSE,
          caption = "Taxonomic  count table",
          class = "cell-border stripe",
          options = list(search = FALSE,
                         pageLength = 10)) %>%
  formatCurrency(columns = 7:36,
                 currency = "",
                 mark = ",",
                 digits = 0)
```

# Total counts per sample (i.e. sequencing depth)
```{r seq_depth, fig.width = 10,fig.height = 5}
t1 <- colSums(otu[, 7:ncol(otu)])
t1 <- data.table(SAMPLE_NAME = names(t1),
                 Total = t1)

t2 <- data.table(SAMPLE_NAME = rownames(samples),
                 ID = samples$ID,
                 CAGE = samples$cage,
                 TREATMENT = samples$TREATMENT,
                 Genotype = samples$genotype,
                 WEEK = samples$time)

smpl <- merge(t1,
              t2,
              by = "SAMPLE_NAME")

p1 <- ggplot(smpl,
             aes(x = SAMPLE_NAME,
                 y = Total,
                 fill = TREATMENT,
                 colour = WEEK)) +
  facet_wrap(~ Genotype,
             scale = "free_x") +
  geom_bar(stat = "identity") +
  scale_x_discrete("") +
  scale_y_continuous("Number of Reads") +
  scale_fill_discrete("Treatment") +
  theme(axis.text.x = element_text(angle = 45,
                                   hjust = 1)) 
ggplotly(p1)
```

```{r seq_depth_greyscale, , fig.width = 6, fig.height = 6}
tmp <- copy(smpl)
tmp$WEEK <- factor(tmp$WEEK,
                    levels = c("baseline",
                               "week1",
                               "week8"),
                    labels = c("Week 0",
                               "Week 1",
                               "Week 8"))
tmp$Genotype <- factor(tmp$Genotype,
                       levels = c("widetype",
                                  "nrf2KO"),
                       labels = c("Wild Type",
                                  "Nrf2 KO"))
p1 <- ggplot(tmp,
             aes(x = SAMPLE_NAME,
                 y = Total,
                 group = TREATMENT,
                 fill = TREATMENT)) +
  facet_wrap(~ Genotype + WEEK,
             scale = "free_x") +
  geom_bar(stat = "identity",
           color = "black") +
  scale_x_discrete("") +
  scale_y_continuous("Number of Reads") +
  scale_fill_grey("Treatment", 
                  start = 0.1, 
                  end = 1,
                  na.value = "red",
                  aesthetics = "fill") +
  theme_bw() + 
  theme(panel.border = element_blank(), 
        panel.grid.major = element_blank(),
        panel.grid.minor = element_blank(), 
        axis.title.x=element_blank(),
        axis.text.x=element_blank(),
        axis.ticks.x=element_blank(),
        legend.position = "top")

tiff(filename = "tmp/seq_depth.tiff",
     height = 6,
     width = 6,
     units = "in",
     res = 600,
     compression = "lzw+p")
print(p1)
graphics.off()

print(p1)
```

# Richness (Alpha diversity)
Shannon index (aka Shannon enthrophy) is calculated as:  
H' = -sum(1 to R)p(i)ln(p(i)) 
When there is exactly 1 type of data (e.g. a single species in the sample), H'=0. The opposite scenario is when there are R>1 species present in the sample in the exact same amounts and H'=ln(R).  
  
Shannon's diversity index was calculated for each sample and ploted over time using the 7,764 from the 13 Phylum above.
  
```{r shannon_vs_depth, fig.height = 5, fig.width = 6}
shannon.ndx <- estimate_richness(ps0,
                                 measures = "Shannon")

shannon.ndx <- data.table(SAMPLE_NAME = rownames(shannon.ndx),
                          shannon.ndx)

smpl <- merge(smpl,
              shannon.ndx,
              by = "SAMPLE_NAME")

p1 <- ggplot(smpl,
             aes(x = Total,
                 y = Shannon,
                 fill = Genotype,
                 shape = WEEK)) +
  geom_point(size = 2) +
  scale_shape_manual(breaks = unique(smpl$WEEK),
                     values = 21:23)

tiff(filename = "tmp/shannon_vs_depth.tiff",
     height = 5,
     width = 6,
     units = "in",
     res = 600,
     compression = "lzw+p")
print(p1)
graphics.off()

ggplotly(p1)
```

Even though ***estimate_richness*** function does not adjust for the sequencing depth, there is no correlation between the index and the sample's sequecing depth. Proceed with the comparison.

# Shannon idex over time
```{r richness, fig.width = 8, fig.height = 5}
p1 <- plot_richness(ps0,
                    x = "time", 
                    measures = "Shannon") +
  facet_wrap(~ genotype) +
  geom_line(aes(group = ID),
            color = "black") +
  geom_point(aes(fill = TREATMENT),
             shape = 21,
             size = 3,
             color = "black") +
  scale_x_discrete("") +
  theme(axis.text.x = element_text(angle = 30,
                                   hjust = 1,
                                   vjust = 1))

ggplotly(p = p1,
         tooltip = c("ID",
                     "value"))

p1 <- p1 + 
  scale_fill_discrete("") +
  theme(legend.position = "top")

tiff(filename = "tmp/shannon.tiff",
     height = 4,
     width = 5,
     units = "in",
     res = 600,
     compression = "lzw+p")
print(p1)
graphics.off()
```

The plot above suggests that the largest differences in alpha diversity (as measured by Shannon's index) are in genotype.

# Average Shannon Index
```{r avg_shannon_plot, fig.width = 8, fig.height = 5}
# Average shannon index by treatment group
tmp <- copy(smpl)

tmp[, mu := mean(Shannon),
    by = list(TREATMENT,
              Genotype,
              WEEK)]
tmp[, sem := sd(Shannon)/sqrt(.N),
    by = list(TREATMENT,
                 Genotype,
                 WEEK)]
tmp <- unique(tmp[, c("TREATMENT",
                      "Genotype",
                      "WEEK",
                      "mu",
                      "sem")])
tmp$WEEK <- factor(tmp$WEEK,
                   levels = c("baseline",
                              "week1",
                              "week8"),
                   labels = c("Week 0",
                              "Week 1",
                              "Week 8"))
tmp$Genotype <- factor(tmp$Genotype,
                       levels = c("widetype",
                                  "nrf2KO"),
                       labels = c("Wild Type",
                                  "Nrf2 KO"))

p1 <- ggplot(tmp,
             aes(x = WEEK,
                 y = mu,
                 ymin = mu - sem,
                 ymax = mu + sem,
                 fill = TREATMENT,
                 group = TREATMENT)) +
  facet_wrap(~ Genotype) +
  geom_errorbar(position = position_dodge(0.4),
                width = 0.4) +
  geom_line(position = position_dodge(0.4)) +
  geom_point(size = 3,
             shape = 21,
             position = position_dodge(0.4)) +
  scale_x_discrete("") +
  scale_y_continuous("Shannon Index") +
  theme(axis.text.x = element_text(angle = 45,
                                   hjust = 1),
        legend.position = "top") 

tiff(filename = "tmp/avg_shannon.tiff",
     height = 5,
     width = 6,
     units = "in",
     res = 600,
     compression = "lzw+p")
print(p1)
graphics.off()

print(p1)
```
  
Test if the richness changed between the baseline and Week 8.  
  
```{r lm_richness}
smpl$TREATMENT <- factor(smpl$TREATMENT,
                         levels = c("DSS",
                                    "Naive",
                                    "DSS+PEITC",
                                    "DSS+Cranberry"))

tmp <- droplevels(smpl[WEEK != "week1"])

m1 <- lm(Shannon  ~ WEEK*(TREATMENT + Genotype),
         # offset = Total,
         data = tmp)
summary(m1)
```
  
```{r lmer_richness}
m2 <- lmer(Shannon  ~ WEEK*(TREATMENT + Genotype) + (1 | ID),
           # offset = Total,
           data = tmp)
summary(m2)
```

# Calculate change in Shannon index from baseline
```{r delta_shannon, fig.width = 7, fig.height = 5}
dd <- smpl
dd[, delta := Shannon - Shannon[WEEK == "baseline"],
   by = ID]
dd$diff <- paste(dd$WEEK,
                 "-baseline",
                 sep = "")

dd <- dd[WEEK != "baseline",]

p1 <- ggplot(dd,
             aes(x = TREATMENT,
                 y = delta,
                 fill = Genotype)) +
  facet_wrap(~ diff) +
  geom_hline(yintercept = 0,
             linetype = "dashed") +
  geom_point(position = position_dodge(0.3),
             shape = 21,
             size = 3) +
  scale_y_continuous("Shannon Index Percent Change from Baseline") +
  theme(axis.text.x = element_text(angle = 45,
                                   hjust = 1))
print(p1)

dd$TREATMENT <- factor(dd$TREATMENT,
                        levels = c("DSS",
                                   "Naive",
                                   "DSS+PEITC",
                                   "DSS+Cranberry"))
dd$Genotype <- factor(dd$Genotype,
                       levels = c("widetype",
                                  "nrf2KO"))

m1 <- lm(delta ~ TREATMENT*Genotype,
         data = dd)
summary(m1)

# No significant interactions, proceed with 2-way analysis
m2 <- lm(delta ~ TREATMENT + Genotype,
         data = dd)
summary(m2)
```

At Week 8 there was significantly smaller increase of alpha diversity from baseline in Nrf2 KO compared to WT, and in DSS+Cranberry compared to DSS only.

# Load aminoacids
```{r aminoacids_data}
aa <- fread("data_sep2019/sep2019_aminoacids.csv")

aa <- aa[!is.na(ID), ]
aa$ID <- paste0(aa$ID,
                aa$CAGE)

smpl1 <- unique(smpl[, c("ID",
                            "TREATMENT",
                            "Genotype")])
smpl1$ID <- as.character(smpl1$ID)
aa <- merge(smpl1,
            aa,
            by = "ID")
aa[, trt_week := paste(TREATMENT,
                       WEEK,
                       sep = "_")]
aa$trt_week <- factor(aa$trt_week,
                      levels = c("Naive_week2",
                                 "Naive_week6",
                                 "DSS_week2",
                                 "DSS_week6",
                                 "DSS+Cranberry_week2",
                                 "DSS+Cranberry_week6" ,
                                 "DSS+PEITC_week2",
                                 "DSS+PEITC_week6"))
```

# Aminoacids by animal, treatment group and timepoint
```{r aminoacids_plots, fig.height = 4, fig.width = 5}
for (i in 8:(ncol(aa) - 1)) {
  tmp <- aa[, c(1, 3, 7, ncol(aa), i), with = FALSE]
  colnames(tmp)[5] <- "Y"
  p1 <- ggplot(tmp,
               aes(x = trt_week,
                   y = Y,
                   fill = Genotype,
                   group = ID)) +
    geom_line(position = position_dodge(0.3)) +
    geom_point(shape = 21,
               size = 3,
               position = position_dodge(0.3)) +
    scale_x_discrete("") +
    scale_y_continuous(colnames(aa)[i]) +
    theme(axis.text.x = element_text(angle = 45,
                                     hjust = 1))
  # tiff(filename = paste0("tmp/",
  #                        colnames(aa)[i],
  #                        ".tiff"),
  #      height = 4,
  #      width = 5,
  #      units = "in",
  #      res = 600,
  #      compression = "lzw+p")
  # print(p1)
  # graphics.off()
  
  print(p1)
}
```

# Aminoacids over time, group averages
```{r aminoacids_avg_plots, fig.height = 4, fig.width = 5}
out <- list()
for (i in 8:27) {
  tmp <- aa[, c(2, 3, 7, i), 
            with = FALSE]
  names(tmp)[4] <- "val"
  
  tmp[, mu := mean(val,
                   na.rm = TRUE),
      by = list(TREATMENT,
                Genotype,
                WEEK)]
  tmp[, sem := sd(val,
                  na.rm = TRUE)/sqrt(.N),
      by = list(TREATMENT,
                Genotype,
                WEEK)]
  out[[i - 7]] <- data.table(Aminoacid = names(aa)[i],
                             unique(tmp[, c("TREATMENT",
                                            "Genotype",
                                            "WEEK",
                                            "mu",
                                            "sem")]))
}
muaa <- rbindlist(out)
muaa$Aminoacid <- factor(muaa$Aminoacid,
                         levels = unique(muaa$Aminoacid))
muaa$Genotype <- factor(muaa$Genotype,
                        levels = c("widetype",
                                   "nrf2KO"),
                        labels = c("Wild Type",
                                   "Nrf2 KO"))

for (i in 1:nlevels(muaa$Aminoacid)) {
  p1 <- ggplot(muaa[Aminoacid == levels(muaa$Aminoacid)[i], ],
               aes(x = WEEK,
                   y = mu,
                   ymin = mu - sem,
                   ymax = mu + sem,
                   fill = TREATMENT,
                   group = TREATMENT)) +
    facet_wrap(~ Genotype) +
    geom_errorbar(position = position_dodge(0.4),
                  width = 0.4) +
    geom_line(position = position_dodge(0.4)) +
    geom_point(size = 3,
               shape = 21,
               position = position_dodge(0.4)) +
    scale_x_discrete("",
                     breaks = c("week2",
                                "week6"),
                     labels = c("Week 2",
                                "Week 6")) +
    scale_y_continuous(levels(muaa$Aminoacid)[i]) +
    theme(axis.text.x = element_text(angle = 45,
                                     hjust = 1),
          legend.position = "top") 
  
  # tiff(filename = paste0("tmp/avg_",
  #                        levels(muaa$Aminoacid)[i],
  #                        ".tiff"),
  #      height = 5,
  #      width = 6,
  #      units = "in",
  #      res = 600,
  #      compression = "lzw+p")
  # print(p1)
  # graphics.off()
  
  print(p1)
}
```

# Aminoacid data PCA
```{r aminoacids_pca}
dt_pca <- aa[, Alanine:glutamine]

# m1 <- prcomp(dt_pca,
#              center = TRUE,
#              scale. = TRUE)

# m1 <- prcomp(dt_pca,
#              center = FALSE,
#              scale. = FALSE)

m1 <- prcomp(dt_pca)

summary(m1)


# Select PC-s to pliot (PC1 & PC2)
choices <- 1:2
# Scores, i.e. points (df.u)
dt.scr <- data.table(m1$x[, choices])
# Add grouping variable
dt.scr$grp <- aa$trt_week
dt.scr$TREATMENT <- aa$TREATMENT
dt.scr$WEEK <- aa$WEEK
dt.scr

# Loadings, i.e. arrows (df.v)
dt.rot <- as.data.frame(m1$rotation[, choices])
dt.rot$feat <- rownames(dt.rot)
dt.rot <- data.table(dt.rot)
dt.rot

dt.load <- melt.data.table(dt.rot,
                           id.vars = "feat",
                           measure.vars = 1:2,
                           variable.name = "pc",
                           value.name = "loading")
dt.load$feat <- factor(dt.load$feat,
                       levels = unique(dt.load$feat))
# Plot loadings
p0 <- ggplot(data = dt.load,
             aes(x = feat,
                 y = loading)) +
  facet_wrap(~ pc,
             nrow = 2) +
  geom_bar(stat = "identity") +
  ggtitle("PC Loadings") +
  theme(plot.title = element_text(hjust = 0.5),
        axis.text.x = element_text(angle = 45,
                                   hjust = 1))
tiff(filename = "tmp/pc.1.2_loadings.tiff",
     height = 5,
     width = 8,
     units = 'in',
     res = 300,
     compression = "lzw+p")
print(p0)
graphics.off()

print(p0)
```

```{r aminoacids_pca_axes}
# Axis labels
u.axis.labs <- paste(colnames(dt.rot)[1:2], 
                     sprintf('(%0.1f%% explained var.)', 
                             100*m1$sdev[choices]^2/sum(m1$sdev^2)))
u.axis.labs
```

```{r aminoacids_biplot, fig.height = 10, fig.width = 10}
# Based on Figure p0, keep only a few variables with high loadings in PC1 and PC2----
# var.keep.ndx <- which(dt.rot$feat %in% c(...))
# Or select all
# var.keep.ndx <- 3:ncol(dt1)
# Use dt.rot[var.keep.ndx,] and dt.rot$feat[var.keep.ndx]
p1 <- ggplot(data = dt.rot,
             aes(x = PC1,
                 y = PC2)) +
  coord_equal() +
  geom_point(data = dt.scr,
             aes(fill = grp),
             shape = 21,
             size = 2,
             alpha = 0.5) +
  geom_segment(aes(x = 0,
                   y = 0,
                   xend = 10*PC1,
                   yend = 10*PC2),
               arrow = arrow(length = unit(1/2, 'picas')),
               # size = 1, 
               color = "black") +
  geom_text(aes(x = 11*PC1,
                y = 11*PC2,
                label = dt.rot$feat),
            # size = 5,
            hjust = 0.5) +
  scale_x_continuous(u.axis.labs[1]) +
  scale_y_continuous(u.axis.labs[2]) +
  scale_fill_discrete(name = "Group") +
  ggtitle("Biplot of Aminoacids") +
  theme(plot.title = element_text(hjust = 0.5,
                                  size = 20))
tiff(filename = "tmp/aminoacids_biplot.tiff",
     height = 10,
     width = 10,
     units = 'in',
     res = 300,
     compression = "lzw+p")
print(p1)
graphics.off()

ggplotly(p1)
```

```{r aminoacids_biplot_by_week, fig.height = 10, fig.width = 10}
p2 <- ggplot(data = dt.rot,
             aes(x = PC1,
                 y = PC2)) +
  coord_equal() +
  geom_point(data = dt.scr,
             aes(fill = WEEK),
             shape = 21,
             size = 2,
             alpha = 0.5) +
  geom_segment(aes(x = 0,
                   y = 0,
                   xend = 10*PC1,
                   yend = 10*PC2),
               arrow = arrow(length = unit(1/2, 'picas')),
               size = 1.2, 
               color = "black") +
  geom_text(aes(x = 11*PC1,
                y = 11*PC2,
                label = dt.rot$feat),
            # size = 5,
            hjust = 0.5) +
  scale_x_continuous(u.axis.labs[1]) +
  scale_y_continuous(u.axis.labs[2]) +
  scale_fill_discrete(name = "Week") +
  ggtitle("Biplot of Aminoacids") +
  theme(plot.title = element_text(hjust = 0.5,
                                  size = 20))
tiff(filename = "tmp/aminoacids_by_week_biplot.tiff",
     height = 10,
     width = 10,
     units = 'in',
     res = 300,
     compression = "lzw+p")
print(p2)
graphics.off()

ggplotly(p2)
```

```{r aminoacids_biplot_by_trt, fig.height = 10, fig.width = 10}
p2 <- ggplot(data = dt.rot,
             aes(x = PC1,
                 y = PC2)) +
  coord_equal() +
  geom_point(data = dt.scr,
             aes(fill = TREATMENT),
             shape = 21,
             size = 2,
             alpha = 0.5) +
  geom_segment(aes(x = 0,
                   y = 0,
                   xend = 10*PC1,
                   yend = 10*PC2),
               arrow = arrow(length = unit(1/2, 'picas')),
               size = 1.2, 
               color = "black") +
  geom_text(aes(x = 11*PC1,
                y = 11*PC2,
                label = dt.rot$feat),
            # size = 5,
            hjust = 0.5) +
  scale_x_continuous(u.axis.labs[1]) +
  scale_y_continuous(u.axis.labs[2]) +
  scale_fill_discrete(name = "Treatment") +
  ggtitle("Biplot of Aminoacids") +
  theme(plot.title = element_text(hjust = 0.5,
                                  size = 20))
tiff(filename = "tmp/aminoacids_by_trt_biplot.tiff",
     height = 10,
     width = 10,
     units = 'in',
     res = 300,
     compression = "lzw+p")
print(p2)
graphics.off()

ggplotly(p2)
```
# Remove unmapped OTUs
The 311 unmapped OTUs were removed from further analysis (with 7,247 OTUs left).
```{r remove_unmapped_otu_phylum}
ps1 <- subset_taxa(ps0, 
                   !is.na(Phylum))
dim(ps1@otu_table@.Data)
```

# Counts at Phylum level
```{r counts_p, warning=FALSE,echo=FALSE,message=FALSE}
counts_p <- counts_by_tax_rank(dt1 = otu,
                               aggr_by = "Phylum")
setorder(counts_p, -`190919-01`)
datatable(counts_p,
          rownames = FALSE,
          caption = "Taxonomic  count table",
          class = "cell-border stripe",
          options = list(search = FALSE,
                         pageLength = nrow(counts_p))) %>%
  formatCurrency(columns = 2:ncol(counts_p),
                 currency = "",
                 mark = ",",
                 digits = 0)
```

# Relative abundance (%) at Phylum level
```{r ra_p, warning=FALSE,echo=FALSE,message=FALSE}
ra_p <- ra_by_tax_rank(counts = counts_p,
                       pct = FALSE,
                       digit = 4)

datatable(ra_p,
          rownames = FALSE,
          caption = "Taxonomic  count table",
          class = "cell-border stripe",
          options = list(search = FALSE,
                         pageLength = nrow(ra_p))) %>%
  formatPercentage(columns = 2:ncol(counts_p),
                   digits = 2)
```

Remove phyla with relative abundance of >= 1% in less than 10% of samples.

```{r prev_p}
t1 <- data.table(Phylum = ra_p$Phylum,
                 `Number of Samples` = rowSums(ra_p[, 2:ncol(ra_p)] >= 0.01))
t1$`Percent Samples` <-  t1$`Number of Samples`/72

setorder(t1, -`Number of Samples`)
datatable(t1,
          rownames = FALSE,
          caption = "Taxonomic  count table",
          class = "cell-border stripe",
          options = list(search = FALSE,
                         pageLength = nrow(t1))) %>%
  formatPercentage(columns = 3,
                   digits = 1)
```

We will remove Chlamydiae from this analysis.

```{r keep_phyla, warning=FALSE,echo=FALSE,message=FALSE}
keep_p <- t1$Phylum[t1$`Percent Samples` >= 0.1]
# # Keep all
# keep_p <- t1$Phylum

paste0(keep_p, collapse = ", ")

ps1 <- subset_taxa(ps0, 
                   Phylum %in% keep_p )
otu1 <- data.table(ps1@tax_table@.Data,
                   t(ps1@otu_table@.Data))

datatable(head(otu1, 10),
          rownames = FALSE,
          caption = "Taxonomic  count table",
          class = "cell-border stripe",
          options = list(search = FALSE,
                         pageLength = 10)) %>%
  formatCurrency(columns = 7:ncol(otu1),
                 currency = "",
                 mark = ",",
                 digits = 0)
```

7,224 OTUs, down from 7,247 OTUs in the previous table.

# Relative Abundance in Samples at Different Taxonomic Ranks
## 1. Class
```{r counts_c, warning=FALSE,echo=FALSE,message=FALSE,fig.width=15,fig.height=15}
counts_c <- counts_by_tax_rank(dt1 = otu1,
                               aggr_by = "Class")
ra_c <- ra_by_tax_rank(counts_c)

tax.ranks <- unique(otu1[, c("Phylum",
                             "Class")])

ra_c <- merge(tax.ranks,
              ra_c,
              by = "Class")

total <- rowSums(ra_c[, 3:ncol(ra_c)])

ra_c$Class <- factor(ra_c$Class,
                     levels = ra_c$Class[order(total)])

ra_c$Phylum <- factor(ra_c$Phylum,
                      levels = unique(ra_c$Phylum[order(total)]))
tmp <- melt.data.table(data = ra_c,
                       id.vars = 1:2,
                       measure.vars = 3:ncol(counts_c),
                       variable.name = "SAMPLE_NAME",
                       value.name = "RA")

tmp <- merge(data.table(SAMPLE_NAME = smpl$SAMPLE_NAME,
                        WEEK = smpl$WEEK,
                        TREATMENT = smpl$TREATMENT,
                        Genotype = smpl$Genotype),
             tmp,
             by = "SAMPLE_NAME")

# Plot samples
p1 <- ggplot(tmp,
             aes(x = SAMPLE_NAME,
                 y = RA,
                 fill = Class,
                 color = Phylum)) +
  facet_wrap(~ WEEK + TREATMENT + Genotype,
             scales = "free_x",
             nrow = 3) +
  geom_bar(stat = "identity") +
  scale_x_discrete("") +
  scale_y_continuous(expand = c(0, 0)) +
  theme(axis.text.x = element_text(angle = 0,
                                   hjust = 1))
ggplotly(p1+
           theme(legend.position = "none"))
```

```{r means_c, echo = FALSE, warning = FALSE, message = FALSE}
lra <- ra_melt(ra = ra_c,
               samples = smpl,
               sample_name = "SAMPLE_NAME")

mu <- data.table(aggregate(lra$RA,
                           by = list(Week = lra$WEEK,
                                     Treatment = lra$TREATMENT,
                                     Genotype = lra$Genotype,
                                     Class = lra$Class),
                           FUN = "mean"))
mu[, total := sum(x),
   by = "Class"]
ul <- unique(mu[, c("Class", 
                    "total")])
ul <- ul[order(total),]
mu$Class <- factor(mu$Class,
                   level = ul$Class)
mu$total <- NULL

datatable(mu,
          rownames = FALSE,
          caption = "Taxonomic  count table",
          class = "cell-border stripe",
          options = list(search = FALSE,
                         pageLength = 10,
                         order = list(list(3, 'desc')))) %>%
  formatCurrency(columns = 5,
                 currency = "",
                 mark = ",",
                 digits = 2)
```

# PCA at Class level
```{r pca_c_p0, fig.width = 9, fig.height = 7}
dt_pca <- t(ra_c[, 3:ncol(ra_c)])
colnames(dt_pca) <- ra_c$Class
dt_pca_c <- data.table(SAMPLE_NAME = rownames(dt_pca),
                       dt_pca)
dt_pca_c <- merge(smpl,
                  dt_pca_c,
                  by = "SAMPLE_NAME")

# m1 <- prcomp(dt_pca,
#              center = TRUE,
#              scale. = TRUE)

# m1 <- prcomp(dt_pca,
#              center = FALSE,
#              scale. = FALSE)

m1 <- prcomp(dt_pca)
summary(m1)


# Select PC-s to pliot (PC1 & PC2)
choices <- 1:2
# Scores, i.e. points (df.u)
dt.scr <- data.table(m1$x[, choices])
# Add grouping variable
dt.scr$grp <- paste(dt_pca_c$TREATMENT,
                    dt_pca_c$WEEK,
                    dt_pca_c$Genotype)

dt.scr$TREATMENT <- dt_pca_c$TREATMENT
dt.scr$WEEK <- dt_pca_c$WEEK
dt.scr$Genotype <- dt_pca_c$Genotype
dt.scr

# Loadings, i.e. arrows (df.v)
dt.rot <- as.data.frame(m1$rotation[, choices])
dt.rot$feat <- rownames(dt.rot)
dt.rot <- data.table(dt.rot)
dt.rot

dt.load <- melt.data.table(dt.rot,
                           id.vars = "feat",
                           measure.vars = 1:2,
                           variable.name = "pc",
                           value.name = "loading")
dt.load$feat <- factor(dt.load$feat,
                       levels = unique(dt.load$feat))
# Plot loadings
p0 <- ggplot(data = dt.load,
             aes(x = feat,
                 y = loading)) +
  facet_wrap(~ pc,
             nrow = 2) +
  geom_bar(stat = "identity") +
  ggtitle("PC Loadings") +
  theme(plot.title = element_text(hjust = 0.5),
        axis.text.x = element_text(angle = 45,
                                   hjust = 1))
tiff(filename = "tmp/pc.1.2_loadings_class.tiff",
     height = 5,
     width = 8,
     units = 'in',
     res = 300,
     compression = "lzw+p")
print(p0)
graphics.off()

print(p0)
```

```{r pca_axes_c}
# Axis labels
u.axis.labs <- paste(colnames(dt.rot)[1:2], 
                     sprintf('(%0.1f%% explained var.)', 
                             100*m1$sdev[choices]^2/sum(m1$sdev^2)))
u.axis.labs
```

```{r biplot_grp_c, fig.height = 10, fig.width = 10}
# Based on Figure p0, keep only a few variables with high loadings in PC1 and PC2----
# var.keep.ndx <- which(dt.rot$feat %in% c(...))
# Or select all
# var.keep.ndx <- 3:ncol(dt1)
# Use dt.rot[var.keep.ndx,] and dt.rot$feat[var.keep.ndx]
p1 <- ggplot(data = dt.rot,
             aes(x = PC1,
                 y = PC2)) +
  coord_equal() +
  geom_point(data = dt.scr,
             aes(fill = grp),
             shape = 21,
             size = 2,
             alpha = 0.5) +
  geom_segment(aes(x = 0,
                   y = 0,
                   xend = 40*PC1,
                   yend = 40*PC2),
               arrow = arrow(length = unit(1/2, 'picas')),
               # size = 1, 
               color = "black") +
  geom_text(aes(x = 44*PC1,
                y = 44*PC2,
                label = dt.rot$feat),
            # size = 5,
            hjust = 0.5) +
  scale_x_continuous(u.axis.labs[1]) +
  scale_y_continuous(u.axis.labs[2]) +
  scale_fill_discrete(name = "Group") +
  ggtitle("Biplot of Classes of Bacteria") +
  theme(plot.title = element_text(hjust = 0.5,
                                  size = 20)) 
tiff(filename = "tmp/class_biplot_grp.tiff",
     height = 10,
     width = 10,
     units = 'in',
     res = 300,
     compression = "lzw+p")
print(p1)
graphics.off()

ggplotly(p1)
```

```{r biplot_grp_with_avg_c, fig.height = 10, fig.width = 10}
# Find centers of each group
grpg <- "grp"
var1 <- eval(parse(text = paste("dt.scr$",
                                grpg,
                                sep = "")))

cntr <- data.table(Group = unique(var1),
                   PC1 = aggregate(x = dt.scr$PC1,
                                   by = list(var1),
                                   FUN = "mean")$x,
                   PC2 = aggregate(x = dt.scr$PC2,
                                   by = list(var1),
                                   FUN = "mean")$x)
p2 <- p1 + geom_label(data = cntr,
                      aes(x = PC1,
                          y = PC2,
                          label = Group,
                          colour = Group),
                      alpha = 0.5,
                      size = 3) +
  scale_color_discrete(guide = FALSE) +
  theme(legend.position = "none")
print(p2)
```

```{r biplot_genotype_c, fig.height = 10, fig.width = 10}
# Based on Figure p0, keep only a few variables with high loadings in PC1 and PC2----
# var.keep.ndx <- which(dt.rot$feat %in% c(...))
# Or select all
# var.keep.ndx <- 3:ncol(dt1)
# Use dt.rot[var.keep.ndx,] and dt.rot$feat[var.keep.ndx]
p1 <- ggplot(data = dt.rot,
             aes(x = PC1,
                 y = PC2)) +
  coord_equal() +
  geom_point(data = dt.scr,
             aes(fill = Genotype),
             shape = 21,
             size = 2,
             alpha = 0.5) +
  geom_segment(aes(x = 0,
                   y = 0,
                   xend = 40*PC1,
                   yend = 40*PC2),
               arrow = arrow(length = unit(1/2, 'picas')),
               # size = 1, 
               color = "black") +
  geom_text(aes(x = 44*PC1,
                y = 44*PC2,
                label = dt.rot$feat),
            # size = 5,
            hjust = 0.5) +
  scale_x_continuous(u.axis.labs[1]) +
  scale_y_continuous(u.axis.labs[2]) +
  scale_fill_discrete(name = "Group") +
  ggtitle("Biplot of Classes of Bacteria") +
  theme(plot.title = element_text(hjust = 0.5,
                                  size = 20))
tiff(filename = "tmp/class_biplot_genotype.tiff",
     height = 10,
     width = 10,
     units = 'in',
     res = 300,
     compression = "lzw+p")
print(p1)
graphics.off()

ggplotly(p1)
```

```{r biplot_genotype_with_avg_c, fig.height = 10, fig.width = 10}
# Find centers of each group
grpg <- "Genotype"
var1 <- eval(parse(text = paste("dt.scr$",
                                grpg,
                                sep = "")))

cntr <- data.table(Group = unique(var1),
                   PC1 = aggregate(x = dt.scr$PC1,
                                   by = list(var1),
                                   FUN = "mean")$x,
                   PC2 = aggregate(x = dt.scr$PC2,
                                   by = list(var1),
                                   FUN = "mean")$x)
p2 <- p1 + geom_label(data = cntr,
                      aes(x = PC1,
                          y = PC2,
                          label = Group,
                          colour = Group),
                      alpha = 0.5,
                      size = 3) +
  scale_color_discrete(guide = FALSE) +
  theme(legend.position = "none")
print(p2)
```

```{r biplot_week_c, fig.height = 10, fig.width = 10}
# Based on Figure p0, keep only a few variables with high loadings in PC1 and PC2----
# var.keep.ndx <- which(dt.rot$feat %in% c(...))
# Or select all
# var.keep.ndx <- 3:ncol(dt1)
# Use dt.rot[var.keep.ndx,] and dt.rot$feat[var.keep.ndx]
p1 <- ggplot(data = dt.rot,
             aes(x = PC1,
                 y = PC2)) +
  coord_equal() +
  geom_point(data = dt.scr,
             aes(fill = WEEK),
             shape = 21,
             size = 2,
             alpha = 0.5) +
  geom_segment(aes(x = 0,
                   y = 0,
                   xend = 40*PC1,
                   yend = 40*PC2),
               arrow = arrow(length = unit(1/2, 'picas')),
               # size = 1, 
               color = "black") +
  geom_text(aes(x = 44*PC1,
                y = 44*PC2,
                label = dt.rot$feat),
            # size = 5,
            hjust = 0.5) +
  scale_x_continuous(u.axis.labs[1]) +
  scale_y_continuous(u.axis.labs[2]) +
  scale_fill_discrete(name = "Group") +
  ggtitle("Biplot of Classes of Bacteria") +
  theme(plot.title = element_text(hjust = 0.5,
                                  size = 20))
tiff(filename = "tmp/class_biplot_week.tiff",
     height = 10,
     width = 10,
     units = 'in',
     res = 300,
     compression = "lzw+p")
print(p1)
graphics.off()

ggplotly(p1)
```

```{r biplot_week_with_avg_c, fig.height = 10, fig.width = 10}
# Find centers of each group
grpg <- "WEEK"
var1 <- eval(parse(text = paste("dt.scr$",
                                grpg,
                                sep = "")))

cntr <- data.table(Group = unique(var1),
                   PC1 = aggregate(x = dt.scr$PC1,
                                   by = list(var1),
                                   FUN = "mean")$x,
                   PC2 = aggregate(x = dt.scr$PC2,
                                   by = list(var1),
                                   FUN = "mean")$x)
p2 <- p1 + geom_label(data = cntr,
                      aes(x = PC1,
                          y = PC2,
                          label = Group,
                          colour = Group),
                      alpha = 0.5,
                      size = 3) +
  scale_color_discrete(guide = FALSE) +
  theme(legend.position = "none")
print(p2)
```

```{r biplot_trt_c, fig.height = 8, fig.width = 8}
# Based on Figure p0, keep only a few variables with high loadings in PC1 and PC2----
# var.keep.ndx <- which(dt.rot$feat %in% c(...))
# Or select all
# var.keep.ndx <- 3:ncol(dt1)
# Use dt.rot[var.keep.ndx,] and dt.rot$feat[var.keep.ndx]

p1 <- ggplot(data = dt.rot,
             aes(x = PC1,
                 y = PC2)) +
  coord_equal() +
  geom_point(data = dt.scr,
             aes(fill = TREATMENT),
             shape = 21,
             size = 3,
             alpha = 0.5) +
  geom_segment(aes(x = 0,
                   y = 0,
                   xend = 40*PC1,
                   yend = 40*PC2),
               arrow = arrow(length = unit(1/2, 'picas')),
               # size = 1, 
               color = "black") +
  geom_text(aes(x = 44*PC1,
                y = 44*PC2,
                label = dt.rot$feat),
            # size = 5,
            hjust = 0.5) +
  scale_x_continuous(u.axis.labs[1]) +
  scale_y_continuous(u.axis.labs[2]) +
  scale_fill_manual(name = "Treatment",
                    breaks = levels(dt.scr$TREATMENT),
                                    values = c("red",
                                               "green",
                                               "orange",
                                               "blue")) +
                      ggtitle("Biplot of Classes of Bacteria") +
                      theme(plot.title = element_text(hjust = 0.5,
                                                      size = 20))
                    ggplotly(p1)
```

```{r biplot_trt_with_avg_c, fig.height = 8, fig.width = 8}
# Find centers of each group
# grpg <- "TREATMENT"
# var1 <- eval(parse(text = paste("dt.scr$",
#                                 grpg,
#                                 sep = "")))

# cntr <- data.table(Group = levels(var1),
#                    PC1 = aggregate(x = dt.scr$PC1,
#                                    by = list(var1),
#                                    FUN = "mean")$x,
#                    PC2 = aggregate(x = dt.scr$PC2,
#                                    by = list(var1),
#                                    FUN = "mean")$x)

cntr <- data.table(PC1 = aggregate(x = dt.scr$PC1,
                                   by = list(dt.scr$TREATMENT),
                                   FUN = "mean"),
                   PC2 = aggregate(x = dt.scr$PC2,
                                   by = list(var1),
                                   FUN = "mean")$x)
colnames(cntr) <- c("TREATMENT",
                    "PC1",
                    "PC2")

p2 <- p1 + geom_label(data = cntr,
                      aes(x = PC1,
                          y = PC2,
                          label = TREATMENT,
                          colour = TREATMENT),
                      alpha = 0.5,
                      size = 3) +
  scale_color_manual(guide = FALSE,
                    breaks = levels(cntr$TREATMENT),
                                    values = c("red",
                                               "green",
                                               "orange",
                                               "blue")) +
  theme(legend.position = "none")

tiff(filename = "tmp/class_biplot_trt.tiff",
     height = 8,
     width = 8,
     units = 'in',
     res = 300,
     compression = "lzw+p")
print(p2)
graphics.off()

print(p2)
```

```{r multinom_c}
# Covariates only
m1 <- multinom(TREATMENT ~ WEEK + Genotype,
               data = dt.scr)
summary(m1)

prd1 <- predict(m1)

t1 <- table(Predicted = prd1,
            Observed = dt.scr$TREATMENT)

# PC1 alone
m2 <- multinom(TREATMENT ~ PC1,
               data = dt.scr)
summary(m2)

prd2 <- predict(m2)

t2 <- table(Predicted = prd2,
            Observed = dt.scr$TREATMENT)

# PC1 with covariates
m3 <- multinom(TREATMENT ~ PC1 + WEEK + Genotype,
               data = dt.scr)
summary(m3)

prd3 <- predict(m3)

t3 <- table(Predicted = prd3,
            Observed = dt.scr$TREATMENT)

# PC1 + PC2 with covariates
m4 <- multinom(TREATMENT ~ PC1 + PC2 + WEEK + Genotype,
               data = dt.scr)
summary(m4)

prd4 <- predict(m4)

t4 <- table(Predicted = prd4,
            Observed = dt.scr$TREATMENT)

# Confusion tables
datatable(cbind(t1), 
          caption = "Covariates Only")
datatable(cbind(t2),
          caption = "PC1 Only")
datatable(cbind(t3),
          caption = "PC1 with covariates")
datatable(cbind(t4),
          caption = "PC1 + PC2 with covariates")

# Compare models
anova(m1, m3)
anova(m2, m3)
anova(m4, m3)
```

The results suggest that:  
a. Covariates alone (timepoint and genotype) cannot explain the difference between treatments.  
b. Principal Component 1 (PC1) can explain the differences in relative abundance of classes in the samples. The model does not improve significantly by adding the covariates or the PC2. However, the covariates should stay in the model for adjustment, and PC2 slightly improves the predictions.  
c. The full model (PC1 + PC2 + Week + Genotype) correctly classifies 12 out of 18 Naive samples, and 12 out of 18 DSS+Cranburry samples.  
  
Continuing the same analysis at Order, Family and Genus levels.

DO ROC AUC NEST!!!
```{r roc_FROM_KEAP1NRF2_STUDY, height = 7,fig.width = 8}
# # Output probobilities----
# prd1.1 <- predict(m1,
#                   type = "probs")
# prd1.1 <- data.table(ID = df.u$ID,
#                      Treatment = df.u$Treatment,
#                      round(prd1.1,
#                            4))
# 
# prd2.1 <- predict(m2,
#                   type = "probs")
# prd2.1 <- data.table(ID = df.u$ID,
#                      Treatment = df.u$Treatment,
#                      round(prd2.1,
#                            4))
# 
# # Sensitivity/Specificity
# # Tresholds
# trhd <- seq(0, 1, by = 0.01)
# 
# out1 <- list()
# for (i in 1:length(trhd)) {
#   tmp <- apply(prd1.1[, -c(1:2)],
#                MARGIN = 2,
#                FUN = function(a) {
#                  return(a >= trhd[i])
#                })
#   tmp2 <- apply(X = tmp,
#                 MARGIN = 2,
#                 FUN = function(a) {
#                   aggregate(x = a,
#                             by = list(prd1.1$Treatment),
#                             FUN = sum)$x
#                 })
#   tmp2
#   out1[[i]] <- c(sens = sum(diag(tmp2))/nrow(prd1.1),
#                  spec = (sum(tmp2[upper.tri(tmp2)]) + 
#                            sum(tmp2[lower.tri(tmp2)]))/(nrow(tmp)*(ncol(tmp) - 1)))
# }
# out1 <- data.table(do.call("rbind", out1))
# out1 <- unique(out1)
# 
# out2 <- list()
# for (i in 1:length(trhd)) {
#   tmp <- apply(prd2.1[, -c(1:2)],
#                MARGIN = 2,
#                FUN = function(a) {
#                  return(a >= trhd[i])
#                })
#   tmp2 <- apply(X = tmp,
#                 MARGIN = 2,
#                 FUN = function(a) {
#                   aggregate(x = a,
#                             by = list(prd2.1$Treatment),
#                             FUN = sum)$x
#                 })
#   out2[[i]] <- c(sens = sum(diag(tmp2))/nrow(prd2.1),
#                  spec = (sum(tmp2[upper.tri(tmp2)]) + 
#                            sum(tmp2[lower.tri(tmp2)]))/(nrow(tmp)*(ncol(tmp) - 1)))
# }
# out2 <- data.table(do.call("rbind", out2))
# out2 <- unique(out2)
# 
# # ROC
# roc1 <- auc(x = out1$spec,
#             y = out1$sens,
#             from = 0,
#             to = 1)
# 
# roc2 <- auc(x = out2$spec,
#             y = out2$sens,
#             from = 0,
#             to = 1)
# 
# # ROC plot
# plot(out1$sens ~ out1$spec,
#      type = "l",
#      xlim = c(0, 1),
#      ylim = c(0, 1),
#      xlab = "1 - Specificity",
#      ylab = "Sensitivity",
#      col = "blue")
# lines(out2$sens ~ out2$spec,
#       col = "red")
# text(x = c(0.8, 0.8),
#      y = c(0.2, 0.3),
#      label = c(paste("ROC(PC1) = ",
#                      round(roc1, 
#                            3)),
#                paste("\nROC(PC1+PC2) = ",
#                      round(roc2,
#                            3))),
#      col = c("blue",
#              "red"))
# abline(0, 1, lty = 2)
```

```{r means_c_p0, fig.width = 9, fig.height = 7}
mu$Trt_Genotype <- factor(paste(mu$Treatment,
                          mu$Genotype,
                          sep = "_"))

p0 <- ggplot(mu,
             aes(x = Week,
                 y = x,
                 group = Trt_Genotype)) +
  facet_wrap(~ Class,
             scale = "free_y") +
  geom_line(position = position_dodge(0.3)) +
  geom_point(aes(fill = Trt_Genotype),
             shape = 21,
             size = 2,
             alpha = 0.5,
             position = position_dodge(0.3)) +
  scale_x_discrete("") +
  scale_y_continuous("Relative Abundance (%)") +
  theme(legend.position = "top",
        axis.text.x = element_text(angle = 45,
                                   hjust = 1))

tiff(filename = "tmp/wt_class_over_time.tiff",
     height = 5,
     width = 7,
     units = "in",
     res = 600,
     compression = "lzw+p")
print(p0)
graphics.off()

print(p0)
```

```{r means_c_p1, fig.height = 5, fig.width = 9}
p1 <- ggplot(mu,
             aes(x = x,
                 y = Class,
                 color = Trt_Genotype,
                 shape = Week)) +
  geom_point(size = 3,
             alpha = 0.5) +
  geom_vline(xintercept = 1,
             linetype = "dashed") +
  scale_x_continuous("Relative Abundance (%)") 

tiff(filename = "tmp/wt_class_ra.tiff",
     height = 4,
     width = 7,
     units = "in",
     res = 600,
     compression = "lzw+p")
print(p1)
graphics.off()

ggplotly(p1+
  theme(legend.position = "none"))
```

## 2. Order
```{r counts_o, warning=FALSE,echo=FALSE,message=FALSE,fig.width=15,fig.height=15}
counts_o <- counts_by_tax_rank(dt1 = otu1,
                               aggr_by = "Order")
ra_o <- ra_by_tax_rank(counts_o)

tax.ranks <- unique(otu1[, c("Phylum",
                             "Order")])

ra_o <- merge(tax.ranks,
              ra_o,
              by = "Order")

total <- rowSums(ra_o[, 3:ncol(ra_o)])

ra_o$Order <- factor(ra_o$Order,
                     levels = ra_o$Order[order(total)])

ra_o$Phylum <- factor(ra_o$Phylum,
                      levels = unique(ra_o$Phylum[order(total)]))
tmp <- melt.data.table(data = ra_o,
                       id.vars = 1:2,
                       measure.vars = 3:ncol(counts_o),
                       variable.name = "SAMPLE_NAME",
                       value.name = "RA")

tmp <- merge(data.table(SAMPLE_NAME = smpl$SAMPLE_NAME,
                        WEEK = smpl$WEEK,
                        TREATMENT = smpl$TREATMENT,
                        Genotype = smpl$Genotype),
             tmp,
             by = "SAMPLE_NAME")

# Plot samples
p1 <- ggplot(tmp,
             aes(x = SAMPLE_NAME,
                 y = RA,
                 fill = Order,
                 color = Phylum)) +
  facet_wrap(~ WEEK + TREATMENT + Genotype,
             scales = "free_x",
             nrow = 3) +
  geom_bar(stat = "identity") +
  scale_x_discrete("") +
  scale_y_continuous(expand = c(0, 0)) +
  theme(axis.text.x = element_text(angle = 0,
                                   hjust = 1))
ggplotly(p1+
  theme(axis.text.x = element_text(angle = 0,
                                   hjust = 1),
        legend.position = "none"))
```

```{r means_o, echo = FALSE, warning = FALSE, message = FALSE}
lra <- ra_melt(ra = ra_o,
               samples = smpl,
               sample_name = "SAMPLE_NAME")

mu <- data.table(aggregate(lra$RA,
                           by = list(Week = lra$WEEK,
                                     Treatment = lra$TREATMENT,
                                     Genotype = lra$Genotype,
                                     Order = lra$Order),
                           FUN = "mean"))
mu[, total := sum(x),
   by = "Order"]
ul <- unique(mu[, c("Order", 
                    "total")])
ul <- ul[order(total),]
mu$Order <- factor(mu$Order,
                   level = ul$Order)
mu$total <- NULL

datatable(mu,
          rownames = FALSE,
          caption = "Taxonomic  count table",
          class = "cell-border stripe",
          options = list(search = FALSE,
                         pageLength = 10,
                         order = list(list(3, 'desc')))) %>%
  formatCurrency(columns = 5,
                 currency = "",
                 mark = ",",
                 digits = 2)
```

```{r means_o_p0, fig.width = 9, fig.height = 7}
mu$Trt_Genotype <- factor(paste(mu$Treatment,
                                mu$Genotype,
                                sep = "_"))

p0 <- ggplot(mu,
             aes(x = Week,
                 y = x,
                 group = Trt_Genotype)) +
  facet_wrap(~ Order,
             scale = "free_y") +
  geom_line(position = position_dodge(0.3)) +
  geom_point(aes(fill = Trt_Genotype),
             shape = 21,
             size = 2,
             alpha = 0.5,
             position = position_dodge(0.3)) +
  scale_x_discrete("") +
  scale_y_continuous("Relative Abundance (%)") +
  theme(legend.position = "top",
        axis.text.x = element_text(angle = 45,
                                   hjust = 1))

tiff(filename = "tmp/wt_Order_over_time.tiff",
     height = 5,
     width = 7,
     units = "in",
     res = 600,
     compression = "lzw+p")
print(p0)
graphics.off()

print(p0)
```

```{r means_o_p1, fig.height = 7, fig.width = 7}
p1 <- ggplot(mu,
             aes(x = x,
                 y = Order,
                 color = Trt_Genotype,
                 shape = Week)) +
  geom_point(size = 3,
             alpha = 0.5) +
  geom_vline(xintercept = 1,
             linetype = "dashed") +
  scale_x_continuous("Relative Abundance (%)")

tiff(filename = "tmp/wt_Order_ra.tiff",
     height = 4,
     width = 7,
     units = "in",
     res = 600,
     compression = "lzw+p")
print(p1)
graphics.off()

ggplotly(p1 +
           theme(legend.position = "none"))
```

## 3. Family
```{r counts_f, warning=FALSE,echo=FALSE,message=FALSE,fig.width=15,fig.height=15}
counts_f <- counts_by_tax_rank(dt1 = otu1,
                               aggr_by = "Family")
ra_f <- ra_by_tax_rank(counts_f)

tax.ranks <- unique(otu1[, c("Phylum",
                             "Family")])

ra_f <- merge(tax.ranks,
              ra_f,
              by = "Family")

total <- rowSums(ra_f[, 3:ncol(ra_f)])

ra_f$Family <- factor(ra_f$Family,
                     levels = ra_f$Family[order(total)])

ra_f$Phylum <- factor(ra_f$Phylum,
                      levels = unique(ra_f$Phylum[order(total)]))
tmp <- melt.data.table(data = ra_f,
                       id.vars = 1:2,
                       measure.vars = 3:ncol(counts_f),
                       variable.name = "SAMPLE_NAME",
                       value.name = "RA")

tmp <- merge(data.table(SAMPLE_NAME = smpl$SAMPLE_NAME,
                        WEEK = smpl$WEEK,
                        TREATMENT = smpl$TREATMENT,
                        Genotype = smpl$Genotype),
             tmp,
             by = "SAMPLE_NAME")

# Plot samples
p1 <- ggplot(tmp,
             aes(x = SAMPLE_NAME,
                 y = RA,
                 fill = Family,
                 color = Phylum)) +
  facet_wrap(~ WEEK + TREATMENT + Genotype,
             scales = "free_x",
             nrow = 3) +
  geom_bar(stat = "identity") +
  scale_x_discrete("") +
  scale_y_continuous(expand = c(0, 0)) +
  theme(axis.text.x = element_text(angle = 0,
                                   hjust = 1))
ggplotly(p1+
           theme(legend.position = "none"))
```

```{r means_f, echo = FALSE, warning = FALSE, message = FALSE}
lra <- ra_melt(ra = ra_f,
               samples = smpl,
               sample_name = "SAMPLE_NAME")

mu <- data.table(aggregate(lra$RA,
                           by = list(Week = lra$WEEK,
                                     Treatment = lra$TREATMENT,
                                     Genotype = lra$Genotype,
                                     Family = lra$Family),
                           FUN = "mean"))
mu[, total := sum(x),
   by = "Family"]
ul <- unique(mu[, c("Family", 
                    "total")])
ul <- ul[order(total),]
mu$Family <- factor(mu$Family,
                   level = ul$Family)
mu$total <- NULL

datatable(mu,
          rownames = FALSE,
          caption = "Taxonomic  count table",
          class = "cell-border stripe",
          options = list(search = FALSE,
                         pageLength = 10,
                         Family = list(list(3, 'desc')))) %>%
  formatCurrency(columns = 5,
                 currency = "",
                 mark = ",",
                 digits = 2)
```

NOTE: only the first 24 families had large enough counts - ploting only them.  
  
```{r means_f_p0, fig.width = 9, fig.height = 7}
mu$Trt_Genotype <- factor(paste(mu$Treatment,
                                mu$Genotype,
                                sep = "_"))
mu1 <- droplevels(mu[Family %in% levels(mu$Family)[nlevels(mu$Family):(nlevels(mu$Family) - 24)], ])

p0 <- ggplot(mu1,
             aes(x = Week,
                 y = x,
                 group = Trt_Genotype)) +
  facet_wrap(~ Family,
             scale = "free_y") +
  geom_line(position = position_dodge(0.3)) +
  geom_point(aes(fill = Trt_Genotype),
             shape = 21,
             size = 2,
             alpha = 0.5,
             position = position_dodge(0.3)) +
  scale_x_discrete("") +
  scale_y_continuous("Relative Abundance (%)") +
  theme(legend.position = "top",
        axis.text.x = element_text(angle = 45,
                                   hjust = 1))

tiff(filename = "tmp/wt_Family_over_time.tiff",
     height = 7,
     width = 9,
     units = "in",
     res = 600,
     compression = "lzw+p")
print(p0)
graphics.off()

print(p0)
```

```{r means_f_p1, fig.height = 5, fig.width = 9}
p1 <- ggplot(mu1,
             aes(x = x,
                 y = Family,
                 color = Trt_Genotype,
                 shape = Week)) +
  geom_point(size = 3,
             alpha = 0.5) +
  geom_vline(xintercept = 1,
             linetype = "dashed") +
  scale_x_continuous("Relative Abundance (%)") +
  theme(legend.position = "top")

tiff(filename = "tmp/wt_Family_ra.tiff",
     height = 4,
     width = 7,
     units = "in",
     res = 600,
     compression = "lzw+p")
print(p1)
graphics.off()

ggplotly(p1+
           theme(legend.position = "none"))
```

## 4. Genus
```{r counts_g, warning=FALSE,echo=FALSE,message=FALSE,fig.width=15,fig.height=15}
counts_g <- counts_by_tax_rank(dt1 = otu1,
                               aggr_by = "Genus")
ra_g <- ra_by_tax_rank(counts_g)

tax.ranks <- unique(otu1[, c("Phylum",
                             "Genus")])

ra_g <- merge(tax.ranks,
              ra_g,
              by = "Genus")

total <- rowSums(ra_g[, 3:ncol(ra_g)])

ra_g$Genus <- factor(ra_g$Genus,
                     levels = ra_g$Genus[order(total)])

ra_g$Phylum <- factor(ra_g$Phylum,
                      levels = unique(ra_g$Phylum[order(total)]))
tmp <- melt.data.table(data = ra_g,
                       id.vars = 1:2,
                       measure.vars = 3:ncol(counts_g),
                       variable.name = "SAMPLE_NAME",
                       value.name = "RA")

tmp <- merge(data.table(SAMPLE_NAME = smpl$SAMPLE_NAME,
                        WEEK = smpl$WEEK,
                        TREATMENT = smpl$TREATMENT,
                        Genotype = smpl$Genotype),
             tmp,
             by = "SAMPLE_NAME")

# Plot samples
p1 <- ggplot(tmp,
             aes(x = SAMPLE_NAME,
                 y = RA,
                 fill = Genus,
                 color = Phylum)) +
  facet_wrap(~ WEEK + TREATMENT + Genotype,
             scales = "free_x",
             nrow = 3) +
  geom_bar(stat = "identity") +
  scale_x_discrete("") +
  scale_y_continuous(expand = c(0, 0)) +
  theme(axis.text.x = element_text(angle = 0,
                                   hjust = 1))
ggplotly(p1+
           theme(legend.position = "none"))
```

```{r means_g, echo = FALSE, warning = FALSE, message = FALSE}
lra <- ra_melt(ra = ra_g,
               samples = smpl,
               sample_name = "SAMPLE_NAME")

mu <- data.table(aggregate(lra$RA,
                           by = list(Week = lra$WEEK,
                                     Treatment = lra$TREATMENT,
                                     Genotype = lra$Genotype,
                                     Genus = lra$Genus),
                           FUN = "mean"))
mu[, total := sum(x),
   by = "Genus"]
ul <- unique(mu[, c("Genus", 
                    "total")])
ul <- ul[order(total),]
mu$Genus <- factor(mu$Genus,
                   level = ul$Genus)
mu$total <- NULL

datatable(mu,
          rownames = FALSE,
          caption = "Taxonomic  count table",
          class = "cell-border stripe",
          options = list(search = FALSE,
                         pageLength = 10,
                         Genus = list(list(3, 'desc')))) %>%
  formatCurrency(columns = 5,
                 currency = "",
                 mark = ",",
                 digits = 2)
```

```{r means_g_p0, fig.width = 9, fig.height = 7}
mu$Trt_Genotype <- factor(paste(mu$Treatment,
                                mu$Genotype,
                                sep = "_"))
mu1 <- droplevels(mu[Genus %in% levels(mu$Genus)[nlevels(mu$Genus):(nlevels(mu$Genus) - 35)], ])

p0 <- ggplot(mu1,
             aes(x = Week,
                 y = x,
                 group = Trt_Genotype)) +
  facet_wrap(~ Genus,
             scale = "free_y") +
  geom_line(position = position_dodge(0.3)) +
  geom_point(aes(fill = Trt_Genotype),
             shape = 21,
             size = 2,
             alpha = 0.5,
             position = position_dodge(0.3)) +
  scale_x_discrete("") +
  scale_y_continuous("Relative Abundance (%)") +
  theme(legend.position = "top",
        axis.text.x = element_text(angle = 45,
                                   hjust = 1))

tiff(filename = "tmp/wt_Genus_over_time.tiff",
     height = 9,
     width = 12,
     units = "in",
     res = 600,
     compression = "lzw+p")
print(p0)
graphics.off()

print(p0+
           theme(legend.position = "none"))
```

```{r means_g_p1, fig.height = 9, fig.width = 9}
p1 <- ggplot(mu1,
             aes(x = x,
                 y = Genus,
                 color = Trt_Genotype,
                 shape = Week)) +
  geom_point(size = 3,
             alpha = 0.5) +
  geom_vline(xintercept = 1,
             linetype = "dashed") +
  scale_x_continuous("Relative Abundance (%)") +
  theme(legend.position = "top")

tiff(filename = "tmp/wt_Genus_ra.tiff",
     height = 9,
     width = 9,
     units = "in",
     res = 600,
     compression = "lzw+p")
print(p1)
graphics.off()

ggplotly(p1+
           theme(legend.position = "none"))
```

# Session Information
```{r info,eval=TRUE}
sessionInfo()
```